{"id":1254,"date":"2023-07-26T12:16:41","date_gmt":"2023-07-26T12:16:41","guid":{"rendered":"https:\/\/website.prod.unilu.spikeseed.cloud\/fstm-en\/study-programs\/master-in-data-science\/programme\/"},"modified":"2025-10-09T08:49:54","modified_gmt":"2025-10-09T06:49:54","slug":"programme","status":"publish","type":"study-programs","link":"https:\/\/www.uni.lu\/fstm-en\/study-programs\/master-in-data-science\/programme\/","title":{"rendered":"Programme"},"content":{"rendered":"<div class=\"section py-0 static\">\n<section class=\"py-0 wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><!-- SecondaryNavigationMirror --><div class=\"js-secondary-nav--sticky wp-block-unilux-blocks-secondary-navigation\" id=\"94672fec-0334-4e08-8e50-7c9c7afbbdc4\">\n    <nav id=\"secondary-navigation\" role=\"navigation\" aria-label=\"Secondary menu\" class=\"secondary-nav\">\n        <button class=\"secondary-nav__button btn btn-solid--primary w-full\" aria-expanded=\"false\" aria-controls=\"secondary-navigation__list\" title=\"The selected page is Programme\" type=\"button\">\n            <span class=\"btn__name mr-auto\">Programme<\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n                <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\" \/>\n            <\/svg>\n        <\/button>\n\n        <ul id=\"secondary-navigation__list\" class=\"secondary-nav__list\">\n            <li class=\"secondary-nav__item\">\n    <a href=\"https:\/\/www.uni.lu\/fstm-en\/study-programs\/master-in-data-science\/\" target=\"\" rel=\"noreferrer noopener\" class=\"secondary-nav__link \">\n        <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n            <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n        <\/svg>\n        <span class=\"secondary-nav__name\">Overview<\/span>\n    <\/a>\n<\/li>\n<li class=\"secondary-nav__item\">\n    <a href=\"https:\/\/www.uni.lu\/fstm-en\/study-programs\/master-in-data-science\/programme\/\" target=\"\" rel=\"noreferrer noopener\" class=\"secondary-nav__link isactived\" aria-current=\"page\">\n        <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n            <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n        <\/svg>\n        <span class=\"secondary-nav__name\">Programme<\/span>\n    <\/a>\n<\/li>\n<li class=\"secondary-nav__item\">\n    <a href=\"https:\/\/www.uni.lu\/fstm-en\/study-programs\/master-in-data-science\/career\/\" target=\"\" rel=\"noreferrer noopener\" class=\"secondary-nav__link \">\n        <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n            <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n        <\/svg>\n        <span class=\"secondary-nav__name\">Career<\/span>\n    <\/a>\n<\/li>\n<li class=\"secondary-nav__item\">\n    <a href=\"https:\/\/www.uni.lu\/fstm-en\/study-programs\/master-in-data-science\/testimonials\/\" target=\"\" rel=\"noreferrer noopener\" class=\"secondary-nav__link \">\n        <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n            <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n        <\/svg>\n        <span class=\"secondary-nav__name\">Testimonials<\/span>\n    <\/a>\n<\/li>\n<li class=\"secondary-nav__item\">\n    <a href=\"https:\/\/www.uni.lu\/fstm-en\/study-programs\/master-in-data-science\/teaching-staff\/\" target=\"\" rel=\"noreferrer noopener\" class=\"secondary-nav__link \">\n        <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n            <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n        <\/svg>\n        <span class=\"secondary-nav__name\">Teaching staff<\/span>\n    <\/a>\n<\/li>\n<li class=\"secondary-nav__item\">\n    <a href=\"https:\/\/www.uni.lu\/fstm-en\/study-programs\/master-in-data-science\/admissions\/\" target=\"\" rel=\"noreferrer noopener\" class=\"secondary-nav__link \">\n        <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n            <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n        <\/svg>\n        <span class=\"secondary-nav__name\">Admissions<\/span>\n    <\/a>\n<\/li>\n        <\/ul>\n    <\/nav>\n<\/div>\n<!-- end SecondaryNavigationMirror --><\/div><\/section>\n\n\n<section class=\"section section wp-block-unilux-blocks-headline-text-and-image\">\n    \n<div class=\"wp-block-unilux-blocks-wrapper container xl:max-w-screen-xl\">\n<h2 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"master-in-data-science-programme\"\n    >\nMaster in Data Science programme<\/h2>\n\n\n\n<div class=\"wp-block-unilux-blocks-wrapper flex flex-wrap lg:-mx-32\">\n<div class=\"wp-block-unilux-blocks-wrapper w-full lg:w-7\/12 lg:order-2 lg:px-32\">\n<div class=\"wp-block-unilux-blocks-image-video-wrapper\">\n    <figure class=\"wp-block-dev4-reusable-blocks-image  object-fit--cover\">\n    \n<img decoding=\"async\" class=\"wp-block-image unilux-custom-image-block\"\n                alt=\"\"\n            src=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/4\/2023\/07\/UNI-1005-53-scaled.jpg\"\n                srcset=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/4\/2023\/07\/UNI-1005-53-300x222.jpg 300w, https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/4\/2023\/07\/UNI-1005-53-1024x757.jpg 1024w, https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/4\/2023\/07\/UNI-1005-53-768x568.jpg 768w, https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/4\/2023\/07\/UNI-1005-53-1536x1136.jpg 1536w, https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/4\/2023\/07\/UNI-1005-53-2048x1515.jpg 2048w\"\n                style=\"object-position: 50.00% 50.00%; font-family: &quot;object-fit: cover; object-position: 50.00% 50.00%;&quot;; aspect-ratio: 3\/2; object-fit: cover; width: 100%;\"\n        loading=\"lazy\"\n\/>    <\/figure><\/div><\/div>\n\n\n\n<div class=\"wp-block-unilux-blocks-wrapper w-full lg:w-5\/12 lg:order-1 lg:px-32\">\n<p>The first year lays foundations in several disciplines. The second year offers a more open curriculum, with access to various courses and workshops.<br>The last semester is dedicated to an internship \u2013 either in industry or in research \u2013 chosen in line with professional goals.<br>MADS offers refresher courses at the beginning of the Master to help students fill gaps in linear algebra and analysis.<\/p>\n\n\n\n<ul class=\"wp-block-unilux-blocks-custom-buttons btn-list\"><li class=\"wp-block-unilux-blocks-custom-button\"    aria-disabled=\"false\"\n    >\n    <a\n        role=\"link\"\n        aria-disabled=\"false\"\n                    href=\"https:\/\/www.uni.lu\/fstm-en\/study-programs\/master-in-data-science\/admissions\/\"\n                target=\"_self\"\n        class=\"btn btn--primary\"\n            >Admissions<\/a>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n\n\n<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\">\n<p>To graduate from the Master in Data Science, one must have acquired 120 ECTS by successfully completing each of the 9 modules in the program. Each module consists of courses that, upon passing the exams, award ECTS. A module is considered validated if either the courses within it are passed or if the grade for each of the exams is above 5, and the weighted average of these grades is at least 10.&nbsp;<\/p>\n\n\n\n<p>The program offers a wide variety of pedagogical approaches. Depending on the courses, students may be assessed through written assignments, oral presentations, individual or group projects.<\/p>\n\n\n\n<p>The course format is 30, 45, 60 or 90 Teaching Units (TU), with 1 TU equivalent to a duration of 45 minutes.<\/p>\n\n\n\n<p>Download the full programme of the academic year 2025-2026 <a href=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/4\/2025\/10\/09081854\/MADS_ANNEE-2025-2026_INTERNET_091025.pdf\">here <\/a><\/p>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\">\n<h2 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"academic-contents\"\n    >\nAcademic Contents<\/h2>\n\n\n\n<div id=\"tab-nav\" class=\"tab-nav\">\n    <button\n        type=\"button\"\n        class=\"button-toggle btn btn--white w-full\"\n        aria-expanded=\"false\"\n        aria-controls=\"tab-nav__listing\"\n        title=\"Semester 1\"\n    >\n        <span class=\"btn__name mr-auto\">Currently selected: Semester 1<\/span>\n        <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n            <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\" \/>\n        <\/svg>\n    <\/button>\n    <ul\n        id=\"tab-nav__listing\"\n        class=\"tab-nav__listing nav manual\"\n        role=\"tablist\"\n    >\n                <li role=\"presentation\" class=\"tab-nav__item\" key={button.id}>\n            <button\n                type=\"button\"\n                id=\"tab-6b9edc9c-2769-464a-a54c-6c7794c427b8\"\n                data-bs-toggle=\"tab\"\n                data-bs-target=\"#tabpanel-6b9edc9c-2769-464a-a54c-6c7794c427b8\"\n                role=\"tab\"\n                aria-controls=\"tabpanel-6b9edc9c-2769-464a-a54c-6c7794c427b8\"\n                aria-selected=\"false\"\n                aria-current=\"false\"\n                class=\"tab-nav__button\"\n                tabIndex=\"-1\"\n            >\n                <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n                    <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n                <\/svg>\n                <span class=\"tab-nav__name\">Semester 1<\/span>\n            <\/button>\n        <\/li>\n                <li role=\"presentation\" class=\"tab-nav__item\" key={button.id}>\n            <button\n                type=\"button\"\n                id=\"tab-a37055c6-526c-4ec8-a410-5a08a222d07d\"\n                data-bs-toggle=\"tab\"\n                data-bs-target=\"#tabpanel-a37055c6-526c-4ec8-a410-5a08a222d07d\"\n                role=\"tab\"\n                aria-controls=\"tabpanel-a37055c6-526c-4ec8-a410-5a08a222d07d\"\n                aria-selected=\"false\"\n                aria-current=\"false\"\n                class=\"tab-nav__button\"\n                tabIndex=\"-1\"\n            >\n                <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n                    <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n                <\/svg>\n                <span class=\"tab-nav__name\">Semester 2<\/span>\n            <\/button>\n        <\/li>\n                <li role=\"presentation\" class=\"tab-nav__item\" key={button.id}>\n            <button\n                type=\"button\"\n                id=\"tab-95ee437f-ee72-4f30-9493-f85e4dc048de\"\n                data-bs-toggle=\"tab\"\n                data-bs-target=\"#tabpanel-95ee437f-ee72-4f30-9493-f85e4dc048de\"\n                role=\"tab\"\n                aria-controls=\"tabpanel-95ee437f-ee72-4f30-9493-f85e4dc048de\"\n                aria-selected=\"false\"\n                aria-current=\"false\"\n                class=\"tab-nav__button\"\n                tabIndex=\"-1\"\n            >\n                <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n                    <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n                <\/svg>\n                <span class=\"tab-nav__name\">Semester 3<\/span>\n            <\/button>\n        <\/li>\n                <li role=\"presentation\" class=\"tab-nav__item\" key={button.id}>\n            <button\n                type=\"button\"\n                id=\"tab-bf8b32fb-fcc1-4c0c-a79b-b6ea8255cb4e\"\n                data-bs-toggle=\"tab\"\n                data-bs-target=\"#tabpanel-bf8b32fb-fcc1-4c0c-a79b-b6ea8255cb4e\"\n                role=\"tab\"\n                aria-controls=\"tabpanel-bf8b32fb-fcc1-4c0c-a79b-b6ea8255cb4e\"\n                aria-selected=\"false\"\n                aria-current=\"false\"\n                class=\"tab-nav__button\"\n                tabIndex=\"-1\"\n            >\n                <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n                    <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n                <\/svg>\n                <span class=\"tab-nav__name\">Semester 4<\/span>\n            <\/button>\n        <\/li>\n            <\/ul>\n<\/div>\n<div class=\"tab-content\">\n    <section\n    id=\"tabpanel-6b9edc9c-2769-464a-a54c-6c7794c427b8\"\n    aria-labelledby=\"tab-6b9edc9c-2769-464a-a54c-6c7794c427b8\"\n    class=\"tab-panel fade hidden is-hidden\"\n>\n    <div class=\"wp-block-unilux-blocks-course-offer\">\n    <h3>Course offer for Semestre 1 (2026-2027 Winter)<\/h3>\n    \n<div id=\"accordion-module-6a75ddc3741b1\" class=\"accordion accordion--theme\">\n            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc3741e3\"\n        >\n            <span class=\"accordion__title\">\n                Probability: theory and models                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            6                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-31                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc3741e3\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc3741b1\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Probability: theory and models<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 6<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-31<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 1 Mathematics for Data Science<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul><br><li>At the end of the course, a student should be familiar with the basic concepts of probability theory (event, random variables, distributions) and master the tools that allow him or her to make calculations (calculations of expectations, variances, distributions etc).\u00a0<\/li><br><li>The student should also be able to master the law of the large numbers as well as the central limit theorem.\u00a0<\/li><br><li>He or she must be able to calculate limit distributions of some sequence of random variables by means of the Delta method and Slutsky\u2019s lemma.\u00a0<\/li><br><li>Given an integral, the student should also be able to provide a probabilistic approximation of it and assess the probability of error.\u00a0\u00a0<\/li><br><li>The student should be able to provide a qualitative and quantitative analysis of some random phenomena which are modeled by Markov chains.\u00a0<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>The first part of the course introduces the basic concepts and tools associated with Probability Theory.\u00a0<\/p><ul><li>What is an event, the probability of an event, a random variable?<\/li><li>What are the main notations associated with probability theory?<\/li><li>What is a law or a distribution?\u00a0<\/li><li>The main probability distributions: Bernoulli, binomial, Poisson, uniform, exponential, Cauchy, Gaussian. What random phenomena do they model?\u00a0<\/li><li>What is a density with respect to the Lebesgue measure? What is a density with respect to the counting measure?\u00a0<\/li><li>What is the distribution of a random variable?<\/li><li>What is the expectation (or mean) of a random variable? How can we interpret it?\u00a0<\/li><li>What is the variance of a random variable?<\/li><li>How to calculate the probability that a random variable belongs to a set from its distribution? How to calculate the expectation of a function of a given random variable from its distribution?\u00a0<\/li><li>What is a distribution function? What are its properties? What link densities and distribution functions?\u00a0<\/li><li>What are the quantiles of a real-valued random variable? What are the median and quartiles of a random variable?<\/li><li>The main inequalities: the Markov inequality, the Bienaym\u00e9-Tchebychev inequality, the Jensen inequality.<\/li><li>What does it mean that two random variables are independent? What is the density of a pair of independent random variables? How to generalize to n independent random variables?\u00a0<\/li><li>What is a random vector? What are the marginal distributions? How to calculate them?<\/li><li>How to calculate the distributions of random variables (from distribution functions, change of variables)<\/li><li>What does it mean that a sequence of random variables converges almost surely? In probability? In distribution? What are the connections between these convergence modes?<\/li><li>What is Law of large numbers? What is the Monte Carlo method for calculating integrals?<\/li><li>What is the central limit theorem? What is the Delta method and Slutsky\u2019s lemma?<\/li><\/ul><p>In the second part of the course, we study Markov Chains with values in a finite or countable space. We see the following concepts and tools.<\/p><ul><li>What is a Markov chain? A transition matrix? An invariant measure?<\/li><li>What is a recurrent state?\u00a0 A transient state? How can we classify the states?\u00a0<\/li><li>What is a stopping time? What the strong Markov property?\u00a0<\/li><li>What is the Ergodic Theorem?\u00a0<\/li><\/ul><p>We show how these notions can be used to study random walks, the gambler\u2019s ruin and the spread of pandemics (among others examples).<\/p>\u00a0                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        First session<br><br>Combined assessment (end of course assessment + continuous assessment).<br>Midterm exam and a final exam during the exam season.<br>Retake exam<br>For those of you who are retaking the exam, the final grade will be an oral exam which is organized during the exam season.\u00a0                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Note &#8211; Literatur<br><\/p><br><p>Measure Theory, Probability, and Stochastic Processes<br>Jean-Fran\u00e7ois Legall, SPRINGER<\/p>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc374209\"\n        >\n            <span class=\"accordion__title\">\n                Optimization and probabilistic methods for machine learning                 <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-2                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc374209\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc3741b1\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Optimization and probabilistic methods for machine learning <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-2<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 1 Mathematics for Data Science<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            <p><br>Functional analysis in finite-dimensional<br><br><br><\/p><br><p>A successful student should be able to:<\/p><br><ul><br><li>Prove that a function defined on Rd is differentiable and compute its derivative.<\/li><br><li>Determine the gradient, Hessian matrix of a function twice differentiable on Rd. <\/li><br><li>Prove that a function is convex.<\/li><br><li>Apply the Hilbert projection theorem. <\/li><br><\/ul><br><p><br>Optimization in Rd<br><br><br><\/p><br><p>A successful student should be able to:<\/p><br><ul><br><li>Apply necessary and sufficient criteria to solve an unconstrained optimization problem.<\/li><br><li>In an optimization problem with constraints of the form h(x)=0\u00a0 and g(x)\u22640: define the associated Lagrangian function, conjugate function, dual problem.<\/li><br><li>Give the necessary Karush-Kuhn-Tucker conditions. Sufficient conditions in convex problems.<\/li><br><li>Solve basic linear programming, quadratic programming, convex problems.<\/li><br><li>Know some algorithms that allow to numerically determine the minimizer of a convex function of a convex domain: Gradient Descent (GD) method, Newton\u2019s method, projected GD.<\/li><br><\/ul><br><p><br>Numerical probability<br><\/p><br><p>A successful student should be able to:<\/p><br><ul><br><li>Know and apply some procedures to simulate a random variable (RV): inverse-transform methods, accept-reject method.<\/li><br><li>Design a Monte-Carlo procedure to compute the expectation of a bounded function of a RV directly, by using importance sampling.<\/li><br><li>Basically study simple Markov chains: determine transient\/recurrent states, compute probabilities, determine (if applicable) invariant measure(s), apply the ergodic theorem.<\/li><br><li>Design a MCMC method (Metropolis-Hasting algorithm) to compute the expectation of a bounded function of a RV.<br><\/li><br><\/ul><br><p>\u00a0<\/p>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>1. Elements of functional analysis in finite-dimensional normed vector spaces<br><\/p><ul><li>Basics of functional analysis<\/li><li>Differential calculus<\/li><li>Convex sets and convex functions<\/li><li>Hilbert projection theorem <\/li><\/ul><p>2. Optimization in Rd<\/p><ul><li>Unconstrained optimization<\/li><li>Optimization with constraints \u2013 convex optimization<\/li><li>Algorithms for optimization<\/li><\/ul><p>3. Numerical probability<\/p><ul><li>Simulation of random variables<\/li><li>Monte Carlo methods (principle, MCMC, Metropolis algorithm)<\/li><\/ul>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Exam modalities for the first session<br><br>Mid-term exam and a written final exam<\/p><br><p><br>Exam modalities for the retake exam<br><br>Written final exam<\/p><br>Absence plan<br><br>As I would like to fix a midterm exam, in case the students will not come, a second midterm exam could be organized only for those absent students enrolled for the first midterm exam.                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Note \/ Literature \/ Bibliography<br><\/p><br><ul><br><li>Functional Analysis, Calculus of Variations and Optimal Control, by Francis Clarke. Springer Science Business Media, 2013.<\/li><br><li>Numerical Probability, by Gilles Pag\u00e8s. Springer Cham, Universitext, 2018.<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc374221\"\n        >\n            <span class=\"accordion__title\">\n                Mathematical tools for signal analysis and processing                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            3                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-3                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc374221\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc3741b1\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Mathematical tools for signal analysis and processing<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 3<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-3<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 1 Mathematics for Data Science<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            <ul><br><li>Being able to understand and manipulate modern mathematical signal analyses and processing tools;<\/li><br><li>Being able to choose between these techniques for efficient data representation in various domains.<br><\/li><br><\/ul>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        This course will introduce mathematical signal analysis and processing tools like Fourier transform, time frequency analyses, wavelet transforms. These tools will then be used for efficient, e.g. sparse, data representation, data compression (e.g. JPEG 2000 image compression standard), approximation, denoising, &#8230;                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Exam modalities for the first session: \u201ccombined\u201d assessment<br><br>Two tests (written problem solving supervised in class), hands on in Octave as homework.<\/p><br><p><br>Exam modalities for the retake exam<br><br>Written problem solving (no practical)<br><\/p><br><p><br>Absence plan<br><\/p><br><ul><br><li>Justified absence for one of both written tests, just the other is taken into account.<\/li><br><li>Justified absence at the 2 supervised written tests, need for a retake exam, without practical.<\/li><br><li>Unjustified absence and not returning practical homework = 0 grade.<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Note \/ Literature \/ Bibliography<br><\/p><br><p>* A wavelet tour of signal processing: the sparse way,\u00a0\u00a0<br>St\u00e9phane MALLAT,<br>Academic Press, 2009<\/p>* Fourier analysis and applications. Filtering, numerical computation, wavelets.\u00a0<br>Claude Gasquet, Patrick Witomski.\u00a0<br>Springer, 30, pp.442, 1999, Texts in Applied Mathematics,                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc374239\"\n        >\n            <span class=\"accordion__title\">\n                Programming with R and PYTHON                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-4                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc374239\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc3741b1\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Programming with R and PYTHON<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-4<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 2 Programming, Data Management<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            <ul><br><li>Perform data manipulations in R and Python\u00a0<\/li><br><li>Understand the principles of reproducible analyses\u00a0<\/li><br><li>Apply descriptive statistics to data\u00a0<\/li><br><li>Knowledge of clustering algorithms and regression\u00a0<\/li><br><li>Principles of machine learning methods and their implementation\u00a0<\/li><br><\/ul>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>The course introduces data science with R and Python and prepares students for more specialized courses. The course introduces basic programming concepts as data types, how to load, store and manipulate data sets, first in R using the tidyverse concepts for data manipulation, then in Python. The course then covers descriptive statistics, confidence intervals and hypothesis testing after an introduction to R and RStudio as developing environment to prepare for basic machine learning using standard libraries. Functional programming is used as the paradigm for data analysis.The last part of the course introduces standard machine techniques and algorithms to solve classification, clustering and regression problems. The course is delivered as a series of lectures and practical exercises, familiarizing students with version control systems.\u00a0\u00a0<\/p>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>First session<br><br><br><\/p><br><ul><br><li>written exam (40%)\u00a0<\/li><br><li>30% project work<\/li><br><li>30% exercises\u00a0<\/li><br><\/ul><br><p><br>Retake exam<br><\/p><br><ul><br><li>oral exam<\/li><br><li>another project<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Note \/ Literature \/ Bibliography\u00a0<br><\/p><br><ul><br><li>\u00a0R for data science (https:\/\/r4ds.had.co.nz)\u00a0<\/li><br><li>\u00a0Python for data science (https:\/\/wesmckinney.com\/book\/)<\/li><br><li>\u00a0Modern Statistics for Modern Biology (https:\/\/web.stanford.edu\/class\/bios221\/)\u00a0<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc374250\"\n        >\n            <span class=\"accordion__title\">\n                Advanced Databeses : SQL, NoSQL &amp; Cloud Systems                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-5                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc374250\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc3741b1\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Advanced Databeses : SQL, NoSQL &amp; Cloud Systems<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 2 Programming, Data Management<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            <p>On successful completion of this course, students are capable to:<\/p><br><ul><br><li>explain both the theoretical foundations and the practical application of current NoSQL and cloud-computing architectures.<\/li><br><li>describe how different concepts concerning the modeling and management of large data collections are implemented on top of these architectures.<\/li><br><li>develop and evaluate various use-case applications based on the above platforms.<\/li><br><\/ul>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul><br><li>Understanding NoSQL Fundamentals<\/li><br><li>NoSQL Database Models and Use Cases<\/li><br><li>Data Modeling in NoSQL<\/li><br><li>Working with NoSQL Databases<\/li><br><li>Scalability, Performance Optimization<\/li><br><li>Hands-on Project and Real-World Applications<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>The course introduces both the theoretical foundations and practical applications concerning the broad area of &#8220;NoSQL Databases &amp; Cloud Computing&#8221;. We specifically focus on current tools and respective application-programming interfaces (APIs) in the context of the Apache Hadoop ecosystems. The course starts by reviewing the functionality of a classical SQL database systems and then moves forward to distributed file systems, including the Google (GFS) and Hadoop (HDFS) distributed file systems, which is followed by a detailed discussion of the MapReduce distributed computing principle with different extensions. We then move on to several recent NoSQL engines and key-value stores, including Redis, HBase, Neo4j and MongoDB, which provide a variety of options for processing different data formats such as text, CSV, XML and JSON.\u00a0<br>The course covers the following topics:<\/p><ul><li>Distributed file systems (GFS &amp; HDFS), session semantics vs. transaction semantics, CAP theorem<\/li><li>Apache Hadoop: distributed computing principles (MapReduce), replication, fault tolerance, backup tasks, custom combiners and partitioners, local aggregation\u00a0<\/li><li>Redis database<\/li><li>Neo4j graph database<\/li><li>Apache HBase: distributed key-value store for very large tabular data, columns and column families, indexing and lookups<\/li><li>MongoDB: API overview, JSON processing, user-defined functions<\/li><\/ul>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>First session &#8211; (final exam and continuous evaluation)<br><\/p><br><ul><br><li>Practical exercises (group solutions): 50%<\/li><br><li>Final written or oral exam (individual): 50%<\/li><br><\/ul><br><p><br>Retake exam<br><\/p><br><ul><br><li>Final written or oral exam (individual): 50 %<\/li><br><li>Final grade formula: MAX(written exam, retake exam)+ practical mark)\/2\u00a0<\/li><br><\/ul><br><br><br><p>\u00a0<\/p>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Background literature announced at the beginning of each course.<br>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc374267\"\n        >\n            <span class=\"accordion__title\">\n                The mathematics of networks: theory and algorithms                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            3                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-8                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc374267\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc3741b1\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> The mathematics of networks: theory and algorithms<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 3<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-8<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 3 Transversal courses<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            <p>On successful completion of the course the students should be able to<\/p><br><ul><br><li>understand the relevance of the topics covered in the course for their applications,<\/li><br><li>master the proofs of the main results of the course,<\/li><br><li>solve problems using the toolkit developed in the course<\/li><br><li>be autonomous in learning in the field of Graph Theory.<\/li><br><\/ul>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>Through a presentation of selected topics, the course aims to be an introduction to graph theory, its applications and its algorithmic aspects. It is designed as a self-contained course and focused on problems pertaining to Data Science. <\/p><p>Possible topics for the course include, but are not limited to<\/p><ul><li>Graphs and digraphs, degree and the degree sequence algorithm<\/li><li>Connectedness, distance, shortest paths and connected components algorithms<\/li><li>Graph matching problems and algorithms<\/li><li>Elements of algebraic graph theory and PageRank algorithm<\/li><li>Graph traversal algorithms<\/li><li>Trees and applications<\/li><li>Minimum spanning tree algorithms<\/li><li>Network flow, min cut \u2013 max flow theorem and Ford\u2013Fulkerson algorithm<\/li><li>Centrality and betweness measures<\/li><li>Cluster analysis<\/li><li>Random Graphs<\/li><\/ul>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>First session<br><\/p>Written exam and homework, and possibly algorithm implementation project during the semester.\u00a0\u00a0<br>Retake exam\u00a0<br>Writen exam<p>\u00a0<\/p>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Note \/ Literature \/ Bibliography<br><\/p><br><ul><br><li>R. Diestel, Graph Theory, Springer, 2017<\/li><br><li>D. Jungnickel, Graphs, Networks and algorithms, Springer 2017<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc37427e\"\n        >\n            <span class=\"accordion__title\">\n                Applied Philosophy of Science and Data Ethics                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            3                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-7                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc37427e\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc3741b1\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Applied Philosophy of Science and Data Ethics<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 3<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-7<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 3 Transversal courses<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            <ol><br><li><br>Critically appraise the goals of science, so students can distinguish prediction, explanation, understanding, and design, and grasp how different views shape what counts as scientific success.<\/li><br><li><br>Justify methodological choices, because data science involves implicit assumptions and conventions, and students must be able to explain their decisions and recognize how methods evolve over time.<\/li><br><li><br>Understand scientific inference, to differentiate between deductive, inductive, and abductive reasoning and relate these to reasoning strategies used in both science and AI systems.<\/li><br><li><br>Grasp the epistemology of uncertainty, to understand how science moves from data to belief and why traditional models like the Hypothetico-Deductive method fall short in data science, where probabilistic reasoning, model comparison, and frameworks like Bayesian and frequentist inference better capture uncertainty.\u00a0<\/li><br><li><br>Acknowledge the limits of data, so students appreciate the role of domain knowledge, assumptions, and the iterative nature of inquiry when moving from data to generalizations.<\/li><br><li><br>Discriminate between correlation and causation, as confusing the two remains a widespread and harmful error in data-driven fields.<\/li><br><li><br>Recognize and mitigate biases, because biases, whether from measurement, sampling, confounding, or algorithms, can distort every stage of the data pipeline.<\/li><br><li><br>Understand the role and limits of models and experiments, since modeling, experimental control, and design decisions shape what can be inferred, especially regarding causality and generalizability.<\/li><br><li><br>Integrate non-epistemic values, because ethical, legal, and social considerations directly impact how data science is practiced and evaluated in real-world contexts.<\/li><br><li><br>Connect concepts, as a meta-goal students should relate philosophical, methodological, and ethical dimensions into a coherent understanding of data science practice.<br><\/li><br><\/ol>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\u00a0<\/p><br><p>\u00a0<\/p><br><p>\u00a0<\/p><br><p>\u00a0<\/p>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        This course aim is to provide the students with guidelines and methodologies to identify epistemic and ethical issues present in data science. We expect the students to develop a critical eye that helps them spot and mitigate such problems in their daily work as data scientists.<br>As much as data science involves automating tasks, we should avoid falling for the automatization of the mental processes we undergo to solve new problems. We data scientists are used to dealing with data as much as we do with the methods used to process such data. Still, rarely, if ever, we stop to question the methodologies that justify employing one method or another. Surprisingly for many, data science is as much about science as about data. We have refined the data acquisition tools and technology stacks used for accomplishing all tasks covered under the data science umbrella. However, below the layers of tools and frameworks that facilitate our daily work, there lies a foundational ground of assumptions, principles, and constraints that shape the way we do data science.<br>During this course, students will learn by example different layers of the scientific method and how they relate to data science and data ethics. Namely, they will learn how the mechanisms behind the data affect the data analysis, and how the different types of scientific inference condition the application of data science solutions and conclusions to other contexts. In this sense, examples of statistical abuse, misconduct and bad visualization will be shown together with their, sometimes catastrophic, collateral consequences.<br>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Combined evaluation (final exam and continuous evaluation)<br><br>First session<br><\/p><br><p>Students&#8217; evaluations will be based on their performance in different types of individual and group exercises as well as test exams during the course weeks.<\/p><br><ul><br><li>Written essays\/reports regarding the course content.<\/li><br><li>Open discussion, test exercises and quiz tests.<\/li><br><li>Depending on time, there may be presentation exercises (slides exposition).<br><\/li><br><\/ul><br><p>The mid-term and final exams will be on paper, without the help of a computer or notes. It will consist of single\/multiple-choice questions as well as essay questions. The student is thereby responsible for producing intelligible answers employing readable handwriting and correct English writing. Importantly, these exams may be more challenging than the individual quizzes and assignments as it mixes chapters and does not allow any help from notes. Therefore, it is strongly recommended to study in-depth the content of the course (slides, questions, extra material, etc.) before the exams.<\/p><br><p>The works will count as follows:<br><\/p><br><ul><br><li>Quizzes 20%<\/li><br><li>Midterm 40%<\/li><br><li>Final exam 40%<\/li><br><\/ul><br><p>To pass the course you must get a weighted average of = 5\/10.<br>Still, students must score at least 4\/10 in each of the exams (midterm and final exam) to compute the final grade.<br><\/p><br><p><br>Retake exam<br><br>The retake exam is a written exam too where quiz grade can help with the same condition as above.<\/p><br><p><br>Absence plan<br><\/p><br><ul><br><li>If justified absent (e.g., medical leave) during midterms: It is possible to arrange another exam date during the semester, otherwise (if unjustified) is counted as failed.<\/li><br><li>If justified (e.g., medical leave) absence during the final written exam: It is possible to arrange another exam date. Otherwise (if unjustified) the course must be repeated.<\/li><br><\/ul><br><p><br>Plagiarism<br><\/p><br><p>Quizzes, assignments and any other pieces of work produced during the course by the students should be written in their own words. Any attempt to copy from internet sources or between the students may be detected and the corresponding disciplinary procedure will be open. I encourage the students to read the document on academic misconduct from UL for further details. Note that the potential sanctions for plagiarism may include:<\/p><br><ul><br><li>The cancellation of all grades obtained in examinations for the module or the entire examination session of the respective semester;<\/li><br><li>A ban for up to five years on taking any examinations leading to the award of a degree, diploma or certificate by the 8xav福利导航;<\/li><br><li>The retroactive withdrawal of the degree, diploma or certificate awarded by the 8xav福利导航.<\/li><br><\/ul><br><p>\u00a0<\/p>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Note \/ Literature \/ Bibliography<br><\/p><br><ul><br><li>Pearl, J., Mackenzie, D. (2018). The book of why: The new science of cause and effect (1st ed.). Basic Books.<\/li><br><li>Okasha, S. (2016). Philosophy of science: A very short introduction. Oxford 8xav福利导航 Press.<\/li><br><li>Cairo, A. (2019). How charts lie: Getting smarter about visual information. WW Norton Company.<\/li><br><li>Vega, C. (2025). Philosophy of Science and Data Ethics. https:\/\/posde.cvega.es<\/li><br><li>Johansson, L. G. (2016). Philosophy of science for scientists. Springer.<\/li><br><li>Huff, D. (1954). How to lie with statistics.<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>    <\/div><\/div><\/section>\n<section\n    id=\"tabpanel-a37055c6-526c-4ec8-a410-5a08a222d07d\"\n    aria-labelledby=\"tab-a37055c6-526c-4ec8-a410-5a08a222d07d\"\n    class=\"tab-panel fade hidden is-hidden\"\n>\n    <div class=\"wp-block-unilux-blocks-course-offer\">\n    <h3>Course offer for Semestre 2 (2025-2026 Summer)<\/h3>\n    \n<div id=\"accordion-module-6a75ddc37cd4a\" class=\"accordion accordion--theme\">\n            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc37cd74\"\n        >\n            <span class=\"accordion__title\">\n                Fundamentals of Statistical Learning                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-9                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc37cd74\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc37cd4a\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Fundamentals of Statistical Learning<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-9<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 4 Mathematics for Statistical Learning <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            <p>\u00a0<\/p><br><p>\u00a0<\/p>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>A successful student should be able to<\/p><br><ul><br><li>Understand the purpose of statistical learning\u00a0<\/li><br><li>Be familiar with the probabilistic tools that enables to assess the risk and the excess risk of a predictor.<\/li><br><li>Be able to evaluate the Rademacher complexity of some sets of predictors<\/li><br><li>Be able to calibrate hyperparameters and benchmark learning methods.<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>Programme<\/p><ul><li>Empirical risk minimization. Bounding the prediction error.\u00a0<\/li><li>Concentration inequalities.<\/li><li>Rademacher Complexity. Vapnik-Chervonenkis classes.\u00a0<\/li><li>Gaussian mean-width.<\/li><li>Case of supervised binary classification.<\/li><li>Prediction in bounded regression.<\/li><li>Regularization approaches (cross-validation, unbiased risk estimation).<\/li><li>Nonparametric statistics and minimax rates.<\/li><li>Learning methods: plug-in, penalised ERM, kernel methods, Lasso, perceptron, Gradient descent, boosting, deep learning.<\/li><li>Unsupervised learning: density estimation, principal component analysis, clustering.<br><\/li><\/ul>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>Final grade will be based on class participation, the presentation of a research paper and a written exam.<br><\/p>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Bibliography<br><\/p><br><ul><br><li>Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, Second Edition, pdf available for download.<\/li><br><li>Boucheron, Bousquet, Lugosi, Theory of classification: a survey of some recent advances, ESAIM Probab. Stat. pdf available for download.<\/li><br><li>Tsybakov, an introduction to nonparametric statistics, Springer<\/li><br><li>Wainwright, High-Dimensional statistics: A non-asymptotic Viewpoint, Cambriddge 8xav福利导航 Press, 2019. https:\/\/people.eecs.berkeley.edu\/~wainwrig\/<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc37cd8f\"\n        >\n            <span class=\"accordion__title\">\n                Mathematical Statistics                 <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-35                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc37cd8f\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc37cd4a\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Mathematical Statistics <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-35<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 4 Mathematics for Statistical Learning <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        In parametric models, a successful student should be is able to establish the main properties (consistency, asymptotic normality, etc) of the most classical estimators, provide (possibly asymptotic) confidence regions and tests between two hypotheses.\u00a0\u00a0                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        The main problems of mathematical statistics: estimation, testing, the notion of risk. Construction of confidence intervals and tests. False discovery rate. The empirical measure and its applications: the moment method, estimation by empirical quantiles, the Kolmokorov-Smirnov Test. The maximum likelihood estimator. Exponential families in one dimension. Introduction to the Bayes paradigm and elements of decision theory.                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>First session<br>A partial written exam and a final written exam.<\/p><br><p><br>Retake exam<br><\/p><br>The retake\u00a0exam will consist of a written test that will be the same as that taken by other students sitting the exam\u00a0for the first time.\u00a0\u00a0                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Literatur<br><\/p><br><ul><br><li><br>For probability theory: Real Analysis and Probability, R.M. Dudley<\/li><br><li>For statistics: Mathematical Statistics, P. Bickel and K. Doksum<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc37cd9c\"\n        >\n            <span class=\"accordion__title\">\n                Statistical Methods for High Dimensional Data                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-11                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc37cd9c\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc37cd4a\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Statistical Methods for High Dimensional Data<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-11<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 4 Mathematics for Statistical Learning <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            The goal of the course is a detailed introduction to modern techniques in high dimensional estimation of linear models and covariance matrices. After successful completion the student should be able to establish the main properties of Lasso and related estimators in the setting of linear regression models, asymptotic results for large random matrices and non-asymptotic properties for high dimensional estimators of covariance matrices.                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul><li>High dimensional regression model <\/li><li>Parameter estimation under constraints: Lasso and relates estimation procedures <\/li><li>Variable selection <\/li><li>Introduction to principal component analysis <\/li><li>Basic elements of random matrix theory: semi-circle law, Marcenko-Pastur distribution, Tracy-Widom distribution <\/li><li>Estimation of high dimensional covariance and precision matrices; hard and soft thresholding<\/li><\/ul>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>First session<br><\/p><br><p>Combined evaluation<br>Midterm + final written exam\u00a0<\/p>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Bibliography<br><\/p><br><ul><br><li>P. B\u00fchlmann and S. van de Geer \u201eStatistics for High-Dimensional Data\u201c<\/li><br><li>C. Giraud \u201cIntroduction to High-Dimensional Statistics\u201d<\/li><br><li>G.W. Anderson, A. Guoinnet and O. Zeitouni \u201eAn Introduction to Random Matrices\u201c<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc37cda8\"\n        >\n            <span class=\"accordion__title\">\n                Big Data Analytics                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MICS2-41                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc37cda8\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc37cd4a\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Big Data Analytics<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MICS2-41<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 5 Big Data Analytics <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            The lecture provides an entry point to large-scale data management and distributed computing principles in recent NoSQL architectures. We start with an overview of distributed file systems and MapReduce in Apache Hadoop and then move on to more advanced analytical tasks based on the machine-learning libraries in Apache Spark. The lecture serves as an ideal basis for further topics in this area (such as Master seminars, projects and theses).                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        &#8211; Students become familiar with the usage of recent Big Data platforms such as Apache Hadoop and Spark<br>&#8211; Student obtain an overview of both the theoretical foundations and practical applications of various Big Data and Machine Learning algorithms<br>&#8211; Students learn how to approach and solve different data-analysis tasks by a number of programming exercises with real-world datasets<br>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        The course consists of a combination of theory-oriented lectures and practical exercises, through which the students are guided by a series of real-world use cases and hands-on examples. Specifically, we focus on the following topics:<br>&#8211;\u00a0\u00a0 \u00a0Distributed File Systems (DFS) and MapReduce in Apache Hadoop<br>&#8211;\u00a0\u00a0 \u00a0Resilient Distributed Data (RDD) objects and DataFrames in Apache Spark<br>&#8211;\u00a0\u00a0 \u00a0Implementation of complex DataFlow programs in Spark using Scala<br>&#8211;\u00a0\u00a0 \u00a0Performing advanced analytical tasks in Spark\u2019s MLlib:<br>\u00a0 o\u00a0\u00a0 \u00a0Distributed clustering and classification of objects<br>\u00a0 o\u00a0\u00a0 \u00a0Decision trees and random forests<br>\u00a0 o\u00a0\u00a0 \u00a0Recommender systems via matrix factorization<br>\u00a0 o\u00a0\u00a0 \u00a0Text analysis via latent semantic indexing<br>\u00a0 o\u00a0\u00a0 \u00a0Geospatial data analysis<br>\u00a0 o\u00a0\u00a0 \u00a0Social-network analysis<br>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Practical exercises: 20%<br>Written midterm exam: 40%<br>Written final exam: 40%                    <\/div>\n                <\/li>\n                \n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc37cdb4\"\n        >\n            <span class=\"accordion__title\">\n                Data visualization                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            3                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-6                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc37cdb4\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc37cd4a\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Data visualization<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 3<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-6<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 5 Big Data Analytics <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            <ul><br><li>Know the Tufte principles such as \u201cdata to ink ratio\u201d, \u201cshow the data\u201d\u00a0<\/li><br><li>Explain the principles of good charts \u2013 labels, use of area, angles and lengths, etc.\u00a0<\/li><br><li>Create publication-ready plots<\/li><br><li>Know all basic chart types and their use as well as some advanced plots<\/li><br><li>Formatting tables and figures for ease of reading<\/li><br><li>Know about data transformation for display purposes (normalization, log-transformation, standard error vs standard deviation)<\/li><br><li>Set-up interactive data analysis (using shiny or plotly)<\/li><br><\/ul>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        The course introduces visualization using common software packages for exploratory data analysis and communication of data-driven findings. Data visualization principles such as data-to-ink ratio and weaknesses and strengths of types of data display will be introduced.\u00a0<br>It will teach best practices for figures, such as labeling of axis, titles, captions, red-green awareness as well as tables. The concepts for the use of ggplot2, seaborn, plotly and matplotlib packages will be discussed. Interactive visualizations will be introduced and discussed.\u00a0                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>First session<br><\/p><br><p>Assignments with a final practical work on an individual data set.<\/p>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Note \/ Literature \/ Bibliography<br><br>Fundamentals of Data Visualization https:\/\/clauswilke.com\/dataviz\/                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc37cdc0\"\n        >\n            <span class=\"accordion__title\">\n                Introduction to Machine Learning Methods and Data Mining                 <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-13                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc37cdc0\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc37cd4a\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Introduction to Machine Learning Methods and Data Mining <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-13<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 6 Introduction to Machine Learning Methods and Data Mining <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            After successfully finishing the course, a student will become familiar with the basics of supervised and unsupervised ML methods, understand their theoretical background, advantages, limitations, and get practical experience in solving real problems employing ML techniques. <br>Particular attention will be focused on the interdisciplinary aspect of ML applications and advantages, which provides an understanding of the nature of the data. <br>Within the course, the students will learn how to implement the basic elements of ML models by themselves, as well as how to use state-of-the-art ML software packages.                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>The main chapters are<\/p><ul><li>Preprocessing of collected data, understanding their structure, visualization. (1 hour)<\/li><li>Introduction into Scikit-Learn and TensorFlow. (7 hours)<\/li><li>Unsupervised methods: clustering, nearest neighbor task, association rules mining; rule- and tree-based classifications. (12 hours)<\/li><li>(Kernel) ridge regression. (4 hours)<\/li><li>Support vector machines. (4 hours)<\/li><li>Artificial neural networks. (12 hours)<\/li><li>Advanced topics: model evaluation and selection, anomaly detection, conformal learning (prediction with guarantees of accuracy), causal inference (identification of causal relationships). (4 hours)<\/li><li>Combining different machine learning methods for solving actual problems in natural sciences. (4 hours)<\/li><li>Presentation of personal projects. (8 hours)<\/li><\/ul>The course will be split into series of lectures with following practical exercises. The ideal schedule will be one day per week in a computer class, where the lecture is directly followed by practical exercises. At the end of the course each student will have to present his individual project.                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        The evaluation will be based on the presence on the lectures and practical exercises (25%), the individual project (50%), and the answers on the questions following the presentation (25%).                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Bibliography<br><\/p><br><ul><br><li>G\u00e9ron, Aur\u00e9lien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O&#8217;Reilly Media, 2019.<\/li><br><li>Andriy Burkov. The Hundred-Page Machine Learning Book, 2019<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc37cdcb\"\n        >\n            <span class=\"accordion__title\">\n                Cybersecurity Tool for Threat Intelligence and Forensics                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            3                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MCYSD-7                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc37cdcb\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc37cd4a\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Cybersecurity Tool for Threat Intelligence and Forensics<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 3<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MCYSD-7<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 7 Optional courses <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            Gain proficiency in using open-source platforms and frameworks (such as MISP, AIL, Flowintel, Kunai) for cyber-threat intelligence (CTI) and digital forensics.<br>Develop a solid workflow for threat information sharing, automation, and structured analysis to improve incident response.<br>Understand the forensic process on both Windows and Linux: evidence collection, analysis, and sandboxing or triaging of malware.<br>Learn to plan a cyber-threat intelligence strategy, set intelligence requirements, and incorporate best practices for data sharing and analysis.<br>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Upon successful completion of this course, students will be able to:<br>Apply MISP for threat data sharing, enrichment, and automation, demonstrating familiarity with MISP\u2019s data model and APIs.<br>Design and execute a simple CTI plan by identifying intelligence requirements, aligning them with organizational objectives, and leveraging intelligence frameworks.<br>Implement open-source frameworks (AIL) to crawl, index, and analyze data sources, extracting actionable threat intelligence.<br>Perform Windows-based forensic investigations, including evidence collection and case management using Flowintel or similar tools.<br>Investigate Linux malware in a sandboxed environment (Kunai), identifying malicious behavior, indicators of compromise (IoCs), and correlation with threat intelligence feeds.<br>Collaborate with peers to share artifacts, advisories, and best practices in an operational threat intelligence environment.<br>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        This course offers a practical and hands-on overview of key open-source tools and frameworks used in threat intelligence and digital forensics. The topics include MISP for threat sharing and automation, cyber-threat intelligence planning and best practices, the AIL framework for information extraction and analytics, as well as forensic analysis on both Windows and Linux platforms using open-source or freely available tools such as Flowintel and Kunai. By the end of the course, students will have developed the skills to collect, analyze, and share threat information effectively, to perform forensic investigations across multiple operating systems, and contribute to open source projects                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>100% project\u00a0<\/p><br><p>Objectives:\u00a0Integrate multiple course components into a cohesive threat intelligence or forensic investigation scenario (e.g., analyzing a malware sample, documenting IoCs, feeding them into MISP, presenting findings).<\/p><br><p>Assessment rules:\u00a0Individual or small-group project. Must produce a written report and a companion github repository holding materials and scripts produced in the frame of the project.<br><\/p><br><p>Assessment criteria:<\/p><br><p>Completeness of the investigation steps<br>Correct usage of relevant tools (MISP, AIL, etc.)<br>Quality of threat intelligence correlation<br>Clarity and professionalism of the final report.<\/p><br>If the student fails the course, they will be required to retake it in full during the upcoming summer semester, as no full resit option is offered for this course.<br><p>\u00a0<\/p>                    <\/div>\n                <\/li>\n                \n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc37cdd7\"\n        >\n            <span class=\"accordion__title\">\n                Introduction to Deep Learning                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MICS2-66                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc37cdd7\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc37cd4a\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Introduction to Deep Learning<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MICS2-66<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 7 Optional courses <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            This course provides students with a high-quality and informed understanding of Deep Learning (DL) models for developing AI-based applications. The course promotes problem solving via design thinking philosophy. The course helps students to develop state-of-the-art competencies to solve many different real-world problems using DL. The course will provide students with a competitive advantage to solve challenging research problems that require dealing with complex search spaces, non-linear relationships within the data, and flexible models that can scale up to thousands or millions of observations. These competencies are also of key importance to many industrial and technological companies. Finally, the course also promotes development of soft skills such as written and verbal communication skills, via final project presentations.                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>* Understanding of DL models for real-world applications.<br>*\u00a0Identify foundational building blocks in DL.<br>*\u00a0Ability to formulate and solve problems using DL.<br><\/p>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        1. Introduction<br>2. Discriminative modeling I: Classification<br>3. Discriminative modeling II: Regression<br>4. Unsupervised and self-supervised learning<br>5. Sequence learning<br>6. Generative modeling<br>7. Reinforcement learning<br>8. Deployment and best practices<br>9. Project presentations<br>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Coding assignments (rank-based grading), project-based learning (final project and oral defense) and final exam (quiz-type).                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Recommended books:<br> F. Chollet. Deep Learning with Python, 2nd ed. 2021. <br> I. Goodfellow, Y. Bengio, A. Courville. Deep Learning, 2016. <br>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc37cde3\"\n        >\n            <span class=\"accordion__title\">\n                Statistical Modelling                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-15                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc37cde3\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc37cd4a\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Statistical Modelling<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-15<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 7 Optional courses <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            The main goal of the course is to raise awareness of model assumptions and critical thinking, and to provide a guide as to how to make reasonable modelling choices.\u00a0                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        This course will deal with two cultures of statistical modelling: top-down via a parametric model, and bottom-up via data-adaptive methods. More concretely, it will cover classical distributions and their limitations, flexible distributions allowing to model complex modern datasets, interpretable machine learning, and the difference between the two modelling cultures.\u00a0                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\u00a0<\/p><br><p><br>First session<br>Written exam on both theory and exercises for 75% of the mark, and midterm exam for 25% of the mark.<\/p><br><p><br>Retake exam<br>Written exam on both theory and exercises. The student can choose whether they wish to keep their midterm exam mark or not.<\/p><br><p>\u00a0<\/p><br><p>\u00a0<\/p><br><p>\u00a0<\/p>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Literatur<br><\/p><br><ul><br><li>Ley, C., Babic, S. and Craens, D. (2021) Flexible models for complex data with applications.<\/li><br><li>Annual Review of Statistics and Its Application 8, 18.1-18.23.<\/li><br><li>Genuer, R. and Poggi, J.-M. (2020) Random Forests with R, Springer.<\/li><br><li>Kleinbaum, D.G. and Klein, M. (2012) Survival Analysis \u2013 A Self-learning Text, Springer.<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc37cdf6\"\n        >\n            <span class=\"accordion__title\">\n                Introduction to Biology for Data Scientists                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-14                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc37cdf6\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc37cd4a\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Introduction to Biology for Data Scientists<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-14<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 7 Optional courses <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            <ul><br><li>structure and function of the cell<\/li><br><li>basics in biochemistry<\/li><br><li>basics of genetics<\/li><br><li>basics of evolution<\/li><br><li>introduction to plant biology<\/li><br><li>introduction to animal biology<\/li><br><li>introduction to ecology<\/li><br><\/ul>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        We will follow the Campbell Biology. The course includes fundamental principles of biochemistry, genetics, molecular biology and cell biology.                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Written exam                    <\/div>\n                <\/li>\n                \n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>    <\/div><\/div><\/section>\n<section\n    id=\"tabpanel-95ee437f-ee72-4f30-9493-f85e4dc048de\"\n    aria-labelledby=\"tab-95ee437f-ee72-4f30-9493-f85e4dc048de\"\n    class=\"tab-panel fade hidden is-hidden\"\n>\n    <div class=\"wp-block-unilux-blocks-course-offer\">\n    <h3>Course offer for Semestre 3 (2026-2027 Winter)<\/h3>\n    \n<div id=\"accordion-module-6a75ddc384717\" class=\"accordion accordion--theme\">\n            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc38473f\"\n        >\n            <span class=\"accordion__title\">\n                Nonparametric Statistics                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-18                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc38473f\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Nonparametric Statistics<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-18<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 8 Advanced courses<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            <p>Upon completion a successful student should be able to<\/p><br><ul><br><li>Construct classical estimators of unknown probability distribution<\/li><br><li>Derive asymptotic properties of empirical distribution functions<\/li><br><li>Perform tests for unknown distribution functions<\/li><br><li>Construct non-parametric estimators of the density<\/li><br><li>Estimate components of a non-linear regression model<\/li><br><li>Understand the concept of minimax estimation <\/li><br><\/ul>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul><br><li>Understanding the concepts of empirical distribution functions, kernel estimation and minimax theory<\/li><br><li>Performing derivation of standard estimation and testing methods in nonparametric statistics <br><\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul><li>Estimation of probability measures<\/li><li>Weak limit theorems for empirical measures<\/li><li>Kolmogorov-Smirnov and Cramer-von Mises tests<\/li><li>Estimation of the density<\/li><li>Nadaraja-Watson estimator and general kernel estimators<\/li><li>Non-linear regression models<\/li><li>Minimax theory<\/li><\/ul>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        First session<br>Solving bi-weekly exercises (40%) and a final exam (60%) will build the overall grade.<br>Retake exam<br>Written exam.<br>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Literatur<br><\/p><br><ul><br><li>A.B. Tsybakov (2009): \u201cIntroduction to Nonparametric Estimation\u201d, Springer.<\/li><br><li>A. Van der Vaart and J. Wellner\u00a0 (1996): \u201cWeak Convergence and Empirical Processes\u201d, Springer.<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc38475a\"\n        >\n            <span class=\"accordion__title\">\n                Parallel Computing                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            4                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            F1_MA_HPC_1-19                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc38475a\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Parallel Computing<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 4<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> F1_MA_HPC_1-19<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 8 Advanced courses<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            The frequency of a core in modern processors has been stagnating for more than a decade now. Hence, it becomes unavoidable to use multiple cores and computers in parallel to accelerate a computation. We start this course with a tour of C++, as it is the go-to language for many high-performance framework and scientific applications. We then study the class of embarrassingly parallel algorithms, using the C++ standard library (std::threads) and the OpenMP specification language. Afterwards, we take a deep dive into the peculiarities and challenges of nondeterminism with shared memory parallelism.                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        \u00a0\u200b \u200b A comprehensive understanding of C++ and shared memory parallelism . We learn the concurrency library of C++ and OpenMP. \u200b\u00a0<br>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ol><li>C++ Language\u00a0<\/li><li>Embarrassingly parallel algorithms\u00a0<\/li><li>Shared-state parallelism and std::threads\u00a0<\/li><li>OpenMP\u00a0<\/li><li>Memory consistency\u00a0<\/li><\/ol>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul><br><li>Mid-term written exam (30%)<\/li><br><li>Final written exam (70%)<\/li><br><li>Final grade: max(F, (M*30 + F*70)\/100) where M is the grade of the mid-term exam and F is the grade of the final exam. That is, the maximum grade between the final grade and the weighted average 30\/70 of the mid-term and final exams.<\/li><br><li>Resit: written exam (100%)<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc384767\"\n        >\n            <span class=\"accordion__title\">\n                Bioinformatics and Network Analysis in Life Sciences                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-20                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc384767\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Bioinformatics and Network Analysis in Life Sciences<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-20<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 8 Advanced courses<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            <ul><br><li>Understanding data structure of biological Omics-Data<\/li><br><li>DNA analysis<\/li><br><li>Single-cell RNA sequencing analysis<\/li><br><li>Heterogeneous data integration<\/li><br><\/ul>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul><br><li>Blast approaches for sequence alignments.<\/li><br><li>Single-cell RNA sequencing workflow<\/li><br><li>Biological network analyses\u00a0<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        The course will give an overview of current challenges in biomedical data management and analysis and introduce bioinformatic approaches and tools to analyze biomedical data. A particular focus will be on sequence alignment for DNA and RNA analyses and on single-cell sequencing approaches to biomedical data including the subsequent representation in networks.\u00a0                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        First session\u00a0<br><br>tba<br>Retake exam\u00a0<br>tba                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Literatur<br><br><br><\/p><br><ul><br><li>E. Klipp: Systems Biology<\/li><br><li>U. Alon: Introduction to Systems Biology<\/li><br><li>A Lesk: Introduction to Bioinformatics<\/li><br><li>S. Strogatz: Non-linear Dynamics and Chaos<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc384773\"\n        >\n            <span class=\"accordion__title\">\n                Functional Analysis                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            6                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            F1_MA_MAT_MMCS2-2                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc384773\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Functional Analysis<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 6<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> F1_MA_MAT_MMCS2-2<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 8 Advanced courses<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            The aim of the course is to provide students with efficient tools to study linear operators between infinite dimensional vector spaces. In particular, this class will deal with normed vector spaces, Banach and Hilbert spaces, with bounded linear operators on normed vector spaces, fundamental principles such as Fourier analysis, Lebesgue integral, Han-Banach Theorem, Uniform Boundedness Principle or Closed Graph Theorem, and with spectral theory of compact (self-adjoint) linear operators.\u00a0                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Students will acquire a solid understanding of functional analysis, its fundamental results and basic techniques. In particular, students will understand applications to measure theory, Fourier theory, and the spectral theorem for (unbounded) operators. Students will know the relevance of a theorem, its underlying motivation and a precise idea of its proof. Hopefully, students will demonstrate capacity for mathematical reasoning through analyzing, proving and explaining concepts from functional analysis.\u00a0                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Functional analysis aims to study infinite-dimensional spaces of functions and features of linear operators on these spaces. Though, from the point of view of Mathematics, functional analysis has its own interest, it plays a crucial role in many related areas, in particular in Physics, Engineering or Finance. Roughly speaking, most \u201creal-life problems\u201d involve non-linear partial differential equations with infinite-dimensional solution spaces of functions (or distributions). This course provides Master students with the basic tools and the fundamental results to develop skills to solve such problems.                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Exam modality for the first session<br><br><\/p><br><ul><br><li>Written test on week of October 28, duration 1 hour, 30% of the final grade,<\/li><br><li>Final written exam (scheduled by the administration), 60% of the final grade,<\/li><br><li>Written homework (problems to be determined), 10% of the final grade.<\/li><br><\/ul><br><p><br>Exam modality for the retake exam<br><br>Retake exam (scheduled by the administration) will be in written form.<\/p><br><p><br>Absence<br><br>A make-up will be possible only for an unavoidable reason supported by a proof.<\/p>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Note \/ Literature \/ Bibliography\u00a0<br><br>Introductory Functional Analysis with Applications, by Erwin Kreyszig.<br>Functional Analysis, Spectral Theory and Applications, by Manfred Einsiedler and Thomas Ward.<br>An Introduction to Fourier Analysis, by Russell L. Herman                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc384780\"\n        >\n            <span class=\"accordion__title\">\n                Analysis of Complex\u00a0Networks                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-21                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc384780\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Analysis of Complex\u00a0Networks<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-21<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 8 Advanced courses<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            The course aims to provide the students an overview of (1) important concepts, theories and computational methods in network science, and (2) recent developments in machine learning for graph data.                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        After the course, the student should develop an intuition on how to model systems as networks and perform reasoning and analysis on real-world network data. The student should achieve a good understanding of complex networks (i.e., network metrics, structural properties, types of networks, and network models). The student should have a good overview on different community detection algorithms. The student should be able to describe different type of epidemic spreading over complex networks and establish a formal background for information diffusion and influence maximization in online social networks. It is more desirable that by the end of the course the student also has a basic understanding of machine learning on graphs.                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Networks are a fundamental concept for modelling complex physical, technological, social, and biological systems. The course will cover the fundamental aspects of networks: network models, methods for describing network structure and measuring networks, community detection, and information diffusion in complex networks. More advanced topics, such as network embedding and graph neural networks (GNNs) and their applications, will be also introduced and discussed. With the course, students will learn how to explore computational algorithms and machine learning techniques to reveal insights of real-world networks.                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Exam modalities for the first session<br><br>Continuous assessment (course projects)<br>Exam modalities for the retake exam<br>Oral exam<\/p><br>Absence plan<br><br>Based on the project complete percentage                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul><br><li>Book \u201cNetworks: An Introduction\u201d, by Mark Newman, 2010<\/li><br><li>Book \u201cNetwork Science\u201d, by Albter Laszlo Barabasi, 2016.<\/li><br><li>Book \u201cGraph Representation Learning\u201d, by William L. Hamilton, 2020.<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc38478c\"\n        >\n            <span class=\"accordion__title\">\n                Bayesian Statistics                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-22                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc38478c\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Bayesian Statistics<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-22<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 8 Advanced courses<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            Provide students with the necessary knowledge of Bayesian statistics, both from a theoretical as well as practical viewpoint.\u00a0                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Handle of the concepts from Bayesian statistics, Bayesian inference, Bayesian modelling, practical handle of Bayesian techniques on Python.                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        This course will provide a thorough introduction and overview on the most important concepts from Bayesian inference, starting with the Bayesian philosophy in contrast to frequentist statistics. We will discuss various choices of prior distributions, Bayesian inference, Bayesian modelling, model checking and comparison (in particular introducing the concept of Bayes factor), and advanced computation. The latter aspect will especially be dealt with in the practicals, where Markov Chain Monte Carlo methods shall be treated.                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        First session<br>Written exam with open questions, both theory and exercises, in particular there will be computational exercises<br>Retake exam<br>Written exam with open questions, both theory and exercises, in particular there will be computational exercises                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Note \/ Literature \/ Bibliography\u00a0<br><\/p><br><ul><br><li>Box, GEP and Tiao, GC (1992) Bayesian Inference in Statistical Analysis. John Wiley and Sons<\/li><br><li>Gelman, A, Carlin, JB, Stern, HS, Dunson, DB, Vehtari, A and Rubin, DB (2013) Bayesian Data Analysis 3rd edition. Chapman Hall\/CRC.<\/li><br><li>Brooks, S, Gelman, A, Jones, GL, Meng, X-L (2011) Handbook of Markov Chain Monte Carlo. Chapman Hall\/CRC<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc384797\"\n        >\n            <span class=\"accordion__title\">\n                Natural Language Processing in Data Science                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-29                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc384797\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Natural Language Processing in Data Science<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-29<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 8 Advanced courses<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            <ol><br><li>To introduce students the fundamentals of Natural Language Processing.<\/li><br><li>Learn the techniques in natural language processing.<\/li><br><li>Be familiar with the natural language generation.<\/li><br><li>Be exposed to Text Mining.<\/li><br><li>Understand major applications of NLP<\/li><br><\/ol>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>On completion of the course, students will have the ability to<\/p><br><ol><br><li>Understand and implement word and syntactic level analysis<\/li><br><li>Extract relation in text.<\/li><br><li>Implement the Python and NLTK libraries<\/li><br><li>Implementing Sematic analysis<\/li><br><li>Implement real-time based analysis<\/li><br><\/ol>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>Chapter 1: Basics in NLP 6 hours<br>Overview: Origins and challenges of NLP-Need of NLP, python and NLTK for NLP, Text Wrangling and cleansing- Text cleansing, sentence splitter, tokenization, stemming, lemmatization, stop word removal, rare word removal, spell correction.<\/p><p>Chapter 2: Text Preprocessing and Morphology 12 Hours<br>Character Encoding, Word Segmentation, Sentence Segmentation, Introduction to Corpora, Corpora Analysis. Inflectional and Derivation Morphology, Morphological analysis and generation using Finite State Automata and Finite State transducer.<\/p><p>Chapter 3: Language Modelling 12 Hours<br>Words: Collocations- Frequency-Mean and Variance \u2013Hypothesis testing: The t test, Hypothesis testing of differences, Pearson\u2019s chi-square test, Likelihood ratios. Statistical Inference: n -gram Models over Sparse Data: Bins: Forming Equivalence Classes- N gram model &#8211; Statistical Estimators- Combining Estimators<\/p><p>Chapter 4: POS Tagging and Text Classification 12 Hours<br>Parts of Speech Tagging \u2013 Tagging in NLP, Sequential tagger, N-gram tagger, Regex tagger, Brill tagger, Machine learning taggers-MEC, HMM, CRF, NER tagger, Types of learning techniques, Text Classification-Sampling, Na\u00efve Bayes, Decision trees, Stochastic gradient descent, Support vector machine, Text clustering<\/p><p>Chapter 5: Syntax and Semantics 12 Hours<br>Shallow Parsing and Chunking, Shallow Parsing with Conditional Random Fields (CRF), Lexical Semantics, WordNet, Thematic Roles, Semantic Role Labelling with CRFs. Statistical Alignment and Machine Translation, Text alignment, Word alignment, Information extraction, Text mining, Information Retrieval, NL interfaces, Sentimental Analysis, Question Answering Systems, Social network analysis.<\/p><p>Chapter 6: Recent Trends and Applications of NLP 6 hours<br>Recent trends in NLP, Applications of NLP:\u00a0 Transforming text, Sentiment Analysis, Information retrieval, text summarization, Question and Answering, Automatic Summarization<\/p>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Continuous evaluation                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Text Books<br><br><br><\/p><br><ol><br><li>Nitin Hardeniya, Jacob Perkins, Deepti Chopra, Nisheeth Joshi, Iti Mathur, \u201cNatural Language Processing: Python and NLTK\u201d, Packt publisher, 2016.<\/li><br><li>Christopher D. Manning and Hinrich Schutze, \u201cFoundations of Natural Language Processing\u201d , 6 th Edition, The MIT Press Cambridge, Massachusetts London, England, 2003<\/li><br><\/ol><br><p>\u00a0<\/p><br><p>\u00a0<\/p>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc3847a4\"\n        >\n            <span class=\"accordion__title\">\n                Advanced topics in applied Machine Learning                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-23                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc3847a4\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Advanced topics in applied Machine Learning<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-23<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 8 Advanced courses<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            <p><br>After successfully finishing the course, a student will become familiar with applied machine-learning and robustification of ML-based systems, understand the key methods and learning algorithms, their advantages and limitations. Students will also get practical experience in setting up robust and secure ML systems.<br>Within the course, the students will learn the fundamentals of metaheuristic search algorithms, which will be used to improve the effectiveness and robustness of ML systems. Additionally, students will learn to implement trustworthy state-of-the-art models including graph neural networks, and generative models. Finally, the students will be capable of attacking and defending ML systems for critical settings (energy, healthcare, finance).\u00a0<br><\/p>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>The course aims at introducing clear-eyed and principled algorithms to engineer robust and secure ML-systems. Meta-heuristics will be presented as they are key algorithms to optimize the search of a solution that applies to the robustification of ML-systems (data augmentation, model generalization). Adversarial testing and learning in realistic settings will also be studied. A variety of ML algorithms (DNN, GNN, GenAI), approaches (self-supervised learning, reinforcement-learning, multi-task learning) and applications (fintech, healthcare energy optimization) will be considered. The students should be able to understand, synthetize and present a research paper in relation to the studied topics. They should also be able to implement a multi-stage project and defend their choices and implementations in publication presentations.\u00a0<\/p><p>The program includes (not necessary in this order):<\/p>Introduction (1 lecture)Graph Learning Representation (1 lecture)\u00a0Generative Models (1 lecture)\u00a0Meta-heuristics Optimization (2 lectures)Trustworthy Machine Learning (2 lectures)Project Milestone (1 lecture)Selected Topics on Applied Machine Learning (3 lectures)\u00a0Final Presentations (2 lectures)                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>The evaluation will be based on practical projects (20% regular milestones, 30% final presentation), presentations of one scientific article (30%), and regular participation in the practical and tutorials (20%). The detailed evaluation format and the exact schedule will be presented at the first lecture.\u00a0<br><\/p>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\u00a0<\/p><br>https:\/\/cs.gmu.edu\/~sean\/book\/metaheuristics\/<br>https:\/\/digital-strategy.ec.europa.eu\/en\/library\/ethics-guidelines-trustworthy-ai<br>https:\/\/en.wikipedia.org\/wiki\/Evolutionary_computation\u00a0                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc3847b0\"\n        >\n            <span class=\"accordion__title\">\n                Introduction to imaging AI with applications in medical imaging                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-37                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc3847b0\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Introduction to imaging AI with applications in medical imaging<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-37<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 8 Advanced courses<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                \n                \n                \n                \n                \n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc3847c0\"\n        >\n            <span class=\"accordion__title\">\n                Programming Machine Learning Algorithms for HPC                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            3                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            F1_MA_HPC_1-16                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc3847c0\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Programming Machine Learning Algorithms for HPC<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 3<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> F1_MA_HPC_1-16<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 8 Advanced courses<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            The main objective of the course is to equip participants with an understanding of machine learning (ML) computational challenges and opportunities. The focus is on practical implementation tailored for high-performance computing environments. The course primarily utilizes the Python programming language, offering participants a versatile and hands-on learning experience.                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul><br><li>Gain a comprehensive theoretical knowledge of machine learning challenges and opportunities.\u00a0<\/li><br><li>Develop practical coding skills by implementing machine learning algorithms from scratch in Python.\u00a0<\/li><br><li>Learn how to adapt machine learning algorithms for efficient execution in HPC environments, focusing on parallelization, distribution, and leveraging the computational capabilities of modern hardware (including GPUs). Employing advanced optimization techniques to enhance computational efficiency.\u00a0<\/li><br><li>Acquire skills in gaining insight in code performance: profiling and optimizing machine learning code, identifying and addressing bottlenecks\u00a0<\/li><br><li>Be able to discuss pros and cons of different computational methods for deploying ML models on large datasets.\u00a0<br><\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        The course is designed for individuals seeking a comprehensive understanding of machine learning (ML) computational challenges and opportunities. We specifically emphasis on their implementation and optimization for high-performance computing environments. The course is conducted using mainly the Python programming language, providing a practical and versatile platform for participants.                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        The evaluation is based on 50% on exercises, 50% exam on lectures. There is no final exam                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Student&#8217;s PC required                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc3847cc\"\n        >\n            <span class=\"accordion__title\">\n                Computational methods\u00a0                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MCMP-65                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc3847cc\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Computational methods\u00a0<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MCMP-65<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 8 Advanced courses<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                \n                \n                \n                \n                \n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc3847d8\"\n        >\n            <span class=\"accordion__title\">\n                Workshop I &quot;Practical Data Science for the private sector: Forecasting for the Supply Chain &amp; How to build an LLM From scratch&quot;                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-24                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc3847d8\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Workshop I &quot;Practical Data Science for the private sector: Forecasting for the Supply Chain &amp; How to build an LLM From scratch&quot;<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-24<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 9 Workshops<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            Forecasting for the Supply-Chain<br><br>This is a complementary half-workshop to the Time-Series half-workshop from \u201cPractical Data Science for the public Sector:\u00a0 Time Series Forecasting and Reproducible Pipelines\u201d.\u00a0 Time series will mostly be time series of counts, and you will learn modeling techniques more specific to those, such as Poisson modeling, as well as more general techniques such as trend\/seasonality\/level decomposition, and outlier detection.\u00a0 That will help you to gain insights into the time series and leverage them for forecasting with explainability in mind.\u00a0 You will learn how to assess forecasting performance and place your results in the context of Supply-Chain to make optimization recommendations.<br>Applied Deep Learning: How to Build an LLM from Scratch<br>This course aims to help students understand the role of AI in the financial industry, including key applications in banking and insurance such as fraud detection, document analysis, and customer interaction. It seeks to refresh core Python skills relevant to machine learning and deep learning, including data manipulation, visualization, and model development using standard libraries. The course will guide students in building a complete LLM training and inference pipeline, providing practical understanding of the GPT-2 architecture, from tokenization and model design to fine-tuning and text generation. Additionally, it will explore how AI systems are deployed in real-world enterprises, with an overview of modern MLOps tools and practices for scalable, reliable, and governed AI deployment.<br>                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Course learning outcomes<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        Forecasting for the Supply-Chain<br><br>Being able to model Supply-Chain time series, forecast supply and demand, measure the forecast accuracy, and write a report with recommendations.<br>Applied Deep Learning: How to Build an LLM from Scratch<br>By the end of the course, learners will be able to master language models for financial applications, gaining a strong conceptual and practical understanding of state-of-the-art Large Language Models (LLMs), with a focus on solving real-world problems in banking and insurance such as fraud detection, summarization, and data extraction. They will become proficient in using industry-standard AI tools effectively, particularly key Python-based tools and libraries such as Numpy, Pandas, Matplotlib, Pytorch and Sklearn to accelerate deep learning workflows and experimentation. Learners will build a GPT-2 style LLM from scratch, mastering the complete pipeline of LLM development from data collection and tokenization, through model architecture and training, to inference and evaluation. They will also apply advanced post-training techniques, understanding and implementing methods like fine-tuning, Reinforcement Learning with Human Feedback (RLHF), and safety evaluation to improve model alignment and performance. Finally, students will develop an MLOps engineering mindset, acquiring the skills to deploy, monitor, and scale AI systems using modern tools and frameworks such as Docker, Kubernetes, Airflow, MLflow, DVC, and GitHub Actions, ensuring robustness, reliability, and reproducibility in production.<br>                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>Forecasting for the Supply Chain (Bertrand Haas)<br>1. Intro to Time Series and Forecasting for Supply Chain.\u00a0 Forecasting counts.\u00a0 (1 hour)<br>2. Time Series Analysis (10 hours)<br>2.1. Univariate vs Multivariate.<br>2.1.1. Aggregation\/de-aggregation.<br>2.2. State-Space Modeling.<br>2.2.1. Trend, Seasonality, Cycles; Stationarity<br>2.2.2. Exponential Smoothing; Holt-Winters;\u00a0\u00a0<br>2.2.3. Kalman filter (tentative); Filtering vs Smoothing vs Forecasting<br>2.3. Outliers.<br>2.3.1. Outlier detection.<br>2.3.2. Extreme value theory.<br>2.4. How to take into account extraneous variables?<br>2.5. Intermittent Time series<br>2.6. Stochastic Processes (Markov Chains, Poisson processes and extensions)<br>2.7. How to simulate data; Where to find real-life data.<br>3. Forecasting (7 hours)<br>3.1. How to measure forecasting performance<br>3.2. Actuals\/Forecast sample distributions; Error distributions; Distribution fit.<br>3.3. Log change of coordinates.\u00a0 Actuals vs Forecasts &#8211;&gt; Log-actuals vs Log-forecasts<br>3.4. Usual performance metrics:\u00a0 MSE\/RMSE, MAE, MAPE, MedAPE, WAPE, MASE, etc. (pros &amp; cons).<br>3.5. What is &#8220;accuracy&#8221;?\u00a0 Accuracy vs Precision (bias vs variance); Bias correction.<br>4. Newer algorithms (2 hours)<br>4.1. Intro to ML\/AI; Deep NN; Big data; Big models + regularization.<br>4.2. A quick tour of new algorithms (DeepAR, Prophet, TFT, Chronos-Bolt, &#8230;)<br>4.3. Makridakis competitions.<br>4.4. Students\u2019 presentation<\/p>Applied Deep Learning: How to Build an LLM from Scratch (Geoffrey Nichil &amp; Alexandre Hotton)<br>1. Introduction to AI and typical use cases in insurance and banking<br>Fraud detection, recommendation systems, chatbots, classification, summarization, data extraction.<br>2. Python essentials for deep learning (cf. prerequesite)<br>Tools and libraries: Numpy, Pandas, Matplotlib, Pytorch, Sklearn<br>3. Build your own LLM (GPT-2) from scratch and fine-tune it<br>&#8211; Supervised learning introduction through a comparative case study<br>&#8211; Datasets and loss functions in ML<br>&#8211; Gradient descent and optimization<br>&#8211; Linear models \u2192 Practical: Housing price prediction (Kaggle)<br>&#8211; Multi-layer perceptrons and backpropagation \u2192 Practical: Build a micrograd-like library<br>&#8211; MNIST training with PyTorch<br>&#8211; NLP history and introduction to transformers<br>&#8211; Tokenization, embeddings, self-attention, dropout<br>&#8211; Transformer block: LayerNorm, residuals, feedforward networks \u2192 Practical: Implement GPT-2 architecture and train on Shakespeare<br>&#8211; LLM inference: greedy decoding, temperature, top-k sampling \u2192 Practical: GPT-2 inference<br>&#8211; Post-training: fine-tuning, RLHF, model upload \u2192 Practical: Fine-tune GPT-2 into a custom assistant<br>&#8211; Going further: Hugging Face, Kaggle, diffusion models, reinforcement learning\u00a0<br>4. Deploying and monitoring an AI use case<br>Airflow, Docker, Kubernetes, MLflow, DVC, GitHub, GitHub Actions, GPU infrastructure, feedback loops, control groups.<p>\u00a0<\/p><p>\u00a0<\/p>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Continuous evaluation<br><br>Remark: Both demi-workshop will treat everything as Continuous evaluation and an average of the two notes will be given at the end.<br>First session<br>Forecasting for the supply chain<br>Short written test at the beginning of each class about the content of the previous class. 50%<br>Project with a short oral presentation and a PowerPoint slide or poster for the last class. 50%<br><br>Applied Deep Learning: How to Build an LLM from Scratch<br>QCM: A multiple-choice test will be included as part of the continuous evaluation. (50%)<br>Project with Presentation: An individual project will be part of the continuous evaluation. Each student will choose a topic from a predefined list related to AI and LLMs and deliver a short oral presentation (3 minutes) supported by a PowerPoint slide deck. (50%)<br>Retake exam\u00a0<br>QCM only. Project grade is kept.<br>Absence plan\u00a0<br>If justified absent during midterms, an alternative assignment will be given.<br>If justified absence during the written exam, an exam will be rescheduled<br><\/p>\u00a0                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Note \/ Literature \/ Bibliography<br><br><br>Forecasting for the Supply-Chain<br><br><br><\/p><br><ul><br><li>&#8220;Data Science for Supply Chain Forecasting&#8221; by Nicolas Vandeput<\/li><br><li>&#8220;Forecasting Principles and Practice&#8221; by Rob J Hyndman, G. Athanasopoulos (free online version:\u00a0 https:\/\/otexts.com\/fpp3\/)<\/li><br><\/ul><br><p><br>Applied Deep Learning<br><\/p><br><ul><br><li>\u201cPython for Data Analysis, 3E\u201d by Wes McKinney : Available online at https:\/\/wesmckinney.com\/book\/<\/li><br><li>\u201cNeural Networks: Zero to Hero\u201d by Andrej Karpathy : Available online at https:\/\/www.youtube.com\/watch?v=VMj-3S1tku0list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ\u00a0<\/li><br><li>\u201cBuild a Large Language Model (From Scratch)\u201d by Sebastian Raschka<\/li><br><\/ul>                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc3847e3\"\n        >\n            <span class=\"accordion__title\">\n                Workshop II &quot;Practical Data Science for the Public Sector: Reproducible Pipelines and Time Series Forecasting&quot;                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-25                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc3847e3\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Workshop II &quot;Practical Data Science for the Public Sector: Reproducible Pipelines and Time Series Forecasting&quot;<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-25<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 9 Workshops<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>Part 1\/2 \u2013 20 hours, Vasja Sivec<\/p><p>\u00a0\u00a0\u00a0\u00a0 1. Introduction to Forecasting Time Series (1 hours = 1 lecture)<br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 a. Introduction<br><\/p><p>\u00a0\u00a0\u00a0\u00a0 2. Traditional Models for Forecasting Time Series (10 hours = 5 lectures + 5 exercise)<br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 a. Basic time series concepts \u2013 stationarity and integrated processes\u00a0 <br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 b. Forecasting Univariate time series &#8211; ARIMA<br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 c. Forecasting Multivariate time series &#8211; VAR<br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 d. Forecasting data with seasonal patterns \u2013 SARIMA and ETS<br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 i. Simple Example with Code <br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ii. Theory<br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 iii. Exercise(s)<\/p><p>\u00a0 \u00a0 \u00a0 3. Neural Networks for Forecasting Time Series (6h = 3 lectures + 3 exercise)<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 a. Introduction<br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0 b. NN Representation<br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0 c. Estimation: Feedforward &amp; Backpropagation <br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 d.\u00a0Forecasting<br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 i. Simple example with Code<br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ii. Theory <br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 iii.\u00a0Exercise(s)<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 4. Latest Findings &amp; advanced models (4h = 4 lectures)<br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 a. Latest Findings (Traditional vs. Machine Learning Approach)<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 b. Advanced models (ARCH, Markow Switching, MIDAS, Time Varying Parameter Models,..; if time)<\/p><p>Part 2\/2 \u2013 20 hours, Bruno Rodrigues<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 5. Introduction to R and functional programming (4 hours)<br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 a. Pure functions\u00a0\u00a0\u00a0<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 b.\u00a0Higher-order functions<br><\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 6. Git and Github (3 hours)<br>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 a. Intro to Git and Github<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0b. Cloning repos<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0c.\u00a0Collaborating<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0d.\u00a0Branching<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0e.\u00a0Pull requests<\/p><p>\u00a0 \u00a0 \u00a0 \u00a0 7. Package development and unit testing (3 hours)<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0a.\u00a0Adding functions<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0b.\u00a0Documenting functions using roxygen<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0c.\u00a0Unit testing package<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0d.\u00a0Test coverage<\/p><p>\u00a0 \u00a0 \u00a0 \u00a0 8.\u00a0Build automation and data products (6 hours)<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 a.\u00a0Build automation essentials<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 b.\u00a0Recording package versions<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 c.\u00a0Building a pipeline<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 d.\u00a0Literate programming with Quarto<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 e.\u00a0Interactive web apps using Shiny<\/p><p>\u00a0 \u00a0 \u00a0 \u00a0 9.\u00a0Self-contained pipelines with Docker and Github Actions (4 hours)<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 a.\u00a0Docker essentials<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 b.\u00a0Building a fully reproducible pipeline<br>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 c.\u00a0Short introduction to Github Actions\u00a0<br><\/p><p>\u00a0<\/p><p>\u00a0<\/p>                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Exam modalities for the first session<br><br>Continuous assessment<\/p><br><p><br>Exam modalities for the retake exam<br><br>\/<\/p><br>Absence plan<br><br>Not needed due to continuous assessment                    <\/div>\n                <\/li>\n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Note<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p><br>Main textbook (students are not required to purchase these textbooks)<\/p><br><p><br>Part 1<br><\/p><br><ul><br><li>Ghysels, E. and Marcellino, M., 2018. Applied economic forecasting using time series methods. Oxford 8xav福利导航 Press.<\/li><br><li>L\u00fctkepohl, H., 2013. Introduction to multiple time series analysis. Springer Science Business Media.<br><\/li><br><\/ul><br><p><br>Part 2<br><br>Textbook for Part 2 is available here: https:\/\/rap4mads.eu\/<br>Programming environment for exercises: Python or R, depending on what the students already know best.<\/p>\u00a0\u00a0                    <\/div>\n                <\/li>\n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc3847ef\"\n        >\n            <span class=\"accordion__title\">\n                Workshop III &quot;Introduction to Urban Digital Twins&quot;                <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            5                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-38                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc3847ef\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc384717\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Workshop III &quot;Introduction to Urban Digital Twins&quot;<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 5<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-38<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 9 Workshops<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> No<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Objectives<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>\n                            Objective and learning outcomes<br>By the end of the course, students will be able to:<br> Specific knowledge pertaining to water-related (urban) digital twin<br>\u2013 Handle and analyze geospatial and temporal datasets relevant to water management.<br>\u2013 Understand the concept of global water scarcity (\u201cwater bankruptcy\u201d) and the challenges<br>of sustainable water management in European and Luxembourgish contexts.<br>\u2013 Describe, model and analyze natural and urban water cycles.<br>\u2013 Assess cumulative effects in water systems (e.g., impacts of multiple stressors over<br>time and space).<br> Knowledge and know-hows pertaining to data management within an administration<br>\u2013 Theoretical knowledge regarding transparency, information security principles, confidentiality,<br>integrity, and availability.<br>\u2013 Principles of digital archiving and GDPR.<br>\u2013 Open Data concept.<br> Methodological toolbox (depending on project selection)<br>\u2013 Use tools for knowledge management (e.g., AGE\u2019s KMS software \u201dCube\u201d), extracting,<br>classifying and using knowledge.<br>\u2013 Use text embeddings in geospatial layers to extract or represent meaning through<br>visualization.<br>\u2013 Optimize processes, finding balance between objectives and human capabilities (linking<br>high level decisions making with practical implementation)<br>\u2013 Understand the importance of quantifying and managing uncertainty in environmental<br>data and water-related decision-making.<br>\u2013 Apply semantic and graph-based approaches to structure and analyze complex and<br>systemic data relationships.<br>\u2013 Use version control for data and code (e.g., Git) and apply change management and<br>versioning principles to data and models.<br>\u2013 Exploit street-level imagery for environmental observation and analysis.<br>\u2013 Critically assess the use of artificial intelligence, including its role, limitations.<br>\u2013 Work with remote sensing data (e.g., LiDAR, Digital Terrain Models (DTM), Digital<br>Surface Models (DSM)).<br>\u2013 Detect and interpret weak signals in complex systems.<br>\u2013 Apply Bayesian methods for probabilistic reasoning and decision-making.<br>\u2013 Model phenomenon (floods, groundwater recharge) and critically assess their implications<br>for decision-making<br> Transversal skills<br>\u2013 Identify the problems and needs of the stakeholders : translate layman terms into<br>data-driven analytical workflows.<br>\u2013 Perform reviews of the literature in the age of LLMs and use it to solve real-life<br>problems<br>\u2013 Apply AGILE project management methods for data science projects.<br>\u2013 Collaborate efficiently in teams using professional data science tools and workflows.<br>\u2013 Evaluate different modelling and analytical approaches under practical constraints<br>such as limited computational resources.<br>\u2013 Apply mathematical reasoning and analytical tools to real-world problems while justifying<br>methodological choices.<br>\u2013 Test and audit analytical results.<br>\u2013 Communicate results effectively through visualizations and structured reports.                        <\/p>\n                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Description<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        This workshop focuses on transforming complex environmental and territorial data into actionable<br>decisions, while leveraging knowledge generated within an administration to inform and<br>improve future decision-making. Students will learn how to collect, structure, analyze, and<br>interpret a combination of geospatial, temporal and semantic data in order to support decisionmaking<br>processes in water management.<br>The course emphasizes practical problem solving based on real-world situations. Students<br>will work in teams on applied projects where objectives, constraints, and deadlines resemble those<br>encountered in professional environments. Particular attention will be given to communication<br>between technical experts (the students) and non-technical stakeholders (group assignments<br>are worded as if non-expert provided them, for student to identify the problem and formulate<br>solutions), translating real needs into data-driven analytical solutions, with a focus on problem<br>statement, resolution and presentation, all while developping autonomy.<br>The workshop is meant to cover the different aspects pertainig to the inception, developement,<br>testing and utilization of an integrated water-related digital twin, exploring several<br>complementary methodological areas including geospatial analysis, semantic networks, graph<br>databases, data visualization, and large language model (to facilitate the user experience), all<br>of which will be performed at different level of analysis, exploring the problems related to scalability.<br>The workshop adopts a multidisciplinary approach, as water management is a broad and<br>complex subject. Because the scope of an urban digital twin is by nature wide reaching, participants<br>will be exposed to a wide range of tools and methods that are transferable to other<br>domains, such as supply chain management, defense, risk management, energy systems management,<br>smart cities, etc.<br>The scope of the workshop is intentionally described in broad terms throughout this document<br>to favor bottom-up decision-making, as long as they are aligned with the vision for the<br>class. In practice, it will be refined and focused based on the number of participants, their skills,<br>and their preferences. This is why one of the initial assignments consists of completing surveys,<br>allowing instructors to assess participants\u2019 expectations and adapt the approach accordingly,<br>both at the beginning of the semester and continuously throughout the course.                    <\/div>\n                <\/li>\n                \n                \n                                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Assessment<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <p>Assessment<br>Assessment is based on continuous evaluation throughout the semester, reflecting both individual<br>engagement and group work. The final grade is composed of the following components:<\/p><br><p> Engagement and active participation (40%): communication in class, ability to propose<br>solutions and react to feedback, and overall proactivity in the practical side of the<br>weekly sessions<br> Individual practical work (30%): homework, implementing the solutions discussed in<br>class presenting them in the following weekly catch-ups<\/p><br><p> Final presentation (30%): clarity, coherence, and depth of the group\u2019s final deliverable.<br>Completion of the weekly feedback survey is mandatory and contributes to the participation<br>component of the final grade.<br><\/p>                    <\/div>\n                <\/li>\n                \n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>    <\/div><\/div><\/section>\n<section\n    id=\"tabpanel-bf8b32fb-fcc1-4c0c-a79b-b6ea8255cb4e\"\n    aria-labelledby=\"tab-bf8b32fb-fcc1-4c0c-a79b-b6ea8255cb4e\"\n    class=\"tab-panel fade hidden is-hidden\"\n>\n    <div class=\"wp-block-unilux-blocks-course-offer\">\n    <h3>Course offer for Semestre 4 (2025-2026 Summer)<\/h3>\n    \n<div id=\"accordion-module-6a75ddc38e9cb\" class=\"accordion accordion--theme\">\n            \n<div class=\"accordion__item\">\n    <h4 class=\"accordion__header\">\n        <button\n                type=\"button\"\n                class=\"accordion__button collapsed\"\n                aria-expanded=\"false\"\n                data-bs-toggle=\"collapse\"\n                data-bs-target=\"#accordion-panel-6a75ddc38e9fe\"\n        >\n            <span class=\"accordion__title\">\n                Internship or Master Thesis                 <span class=\"accordion__data\">\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">ECTS credits:<\/span>\n                        <span class=\"accordion__data__value\">\n                            30                        <\/span>\n                    <\/span>\n                    <span class=\"accordion__data__item\">\n                        <span class=\"accordion__data__name\">Course code:<\/span>\n                        <span class=\"accordion__data__value\">\n                            MA_DS-30                        <\/span>\n                    <\/span>\n                <\/span>\n            <\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-down icon--lg\"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\"><\/use><\/svg>        <\/button>\n    <\/h4>\n\n    <div id=\"accordion-panel-6a75ddc38e9fe\" class=\"accordion__collapse collapse\" data-bs-parent=\"#accordion-module-6a75ddc38e9cb\">\n        <div class=\"accordion__body p-0\">\n            <ul class=\"list-highlighted\">\n                <li class=\"list-highlighted__item items-start\">\n                    <header class=\"list-highlighted__header\">\n                        <h5 class=\"text-18\"><strong>Details<\/strong><\/h5>\n                    <\/header>\n                    <div class=\"list-highlighted__body\">\n                        <ul>\n                            <li><span class=\"text-dark font-heading font-bold\">Course title:<\/span> Internship or Master Thesis <\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Number of ECTS:<\/span> 30<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Course code:<\/span> MA_DS-30<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Module(s):<\/span> Module 10 &#8211; Internship or Master Thesis<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Language:<\/span> EN<\/li>\n                            <li><span class=\"text-dark font-heading font-bold\">Mandatory:<\/span> Yes<\/li>\n                        <\/ul>\n                    <\/div>\n                <\/li>\n\n                <!--<li class=\"list-highlighted__item items-start\">-->\n                <!--    <header class=\"list-highlighted__header\">-->\n                <!--        <h5 class=\"text-18\"><strong>--><!--<\/strong><\/h5>-->\n                <!--    <\/header>-->\n                <!--    <div class=\"list-highlighted__body\">-->\n                <!--        <div class=\"speaker-group\">-->\n                <!--            -->                <!--        <\/div>-->\n                <!--    <\/div>-->\n                <!--<\/li>-->\n\n                \n                \n                \n                \n                \n                \n            <\/ul>\n        <\/div>\n    <\/div>\n<\/div>    <\/div><\/div><\/section>\n<\/div>\n<\/div><\/section>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>MADS\u2019 first year lays foundations in several disciplines. The second year offers a more open curriculum, with access to various courses and workshops.<br \/>\nThe last semester is dedicated to an internship \u2013 either in industry or in research \u2013 chosen in line with professional goals.<br \/>\nMADS offers refresher courses at the beginning of the Master to help students fill gaps in linear algebra and analysis.<\/p>\n","protected":false},"author":282,"featured_media":7401,"parent":1253,"menu_order":0,"template":"","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"Master in Data Science","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"study_program_identifiers":"MA_DS__F1_MA_DS__F1_MA_DS","override_seo_title":false},"type-of-studies":[],"teaching-languages":[],"field-of-interest":[],"organisation":[],"authorship":[282,283],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Master in Data Science: Courses | 8xav福利导航 of Luxembourg<\/title>\n<meta name=\"description\" content=\"The first year in the Master in Data Science programme lays foundations in several disciplines. 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