Details
- Course title: Introduction à la vie professionnelle
- Number of ECTS: 2
- Course code: BPINFOR-87
- Module(s): Module 5.1 * N.C.
- Language: FR
- Mandatory: Yes
Objectives

La fin des études et l’entrée dans la vie professionnelle marquent un tournant essentiel dans la vie. Le cours vise à aider l’étudiant à passer de la vie universitaire à la vie active de manière professionnelle. Cette transition sera facilitée par la présentation de la réalité du monde du travail et des différentes démarches pour décrocher un emploi épanouissant.
Course learning outcomes
Après avoir suivi ce cours, les étudiants seront capables :
- de comprendre les spécificités du marché du travail luxembourgeois.
- d’avoir une vision globale de l’entreprise.
- d’être bien préparé dans leur recherche d’emploi.
- de bien intégrer leur future entreprise.
Description
1. Paysage économique luxembourgeois:
2. Caractéristiques du marché du travail luxembourgeois.
3. Aperçu des principaux secteurs d’activités.
4. Fonctionnement d’une entreprise:
5. Missions des principaux départements.
6. Impact de la transformation digitale des entreprises.
7. Techniques de recherche d’emploi:
8. Description du processus de recrutement d’une entreprise.
9. Identification des attentes du recruteur.
10. Rédaction de la lettre de motivation et du CV.
11. Préparation de l’entretien d’embauche.
12. Le droit du travail:
13. Composantes du contrat de travail.
14. Différents types de contrat de travail.
15. Période d’essai et fin du contrat.
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17. Aperçu des différents types d’entreprises.
18. Vade-mecum des démarches à effectuer.
Assessment

Étudiants débutants et redoublants: Un examen écrit final (100%).
Note
Ouvrage de référence:
- La gestion des talents – C. Dejoux, M. Thévenet (Editions Dunod)
- Les 100 schémas du management – D.Autissier, K.Johnson, L.Giraud (Editions Eyrolles)
- L’art de la reconnaissance au travail – L.Becker (Editions InterEditions)
- Ressources Humaines – J-M Peretti (Editions Vuibert)
- La boîte à outils des soft skills – N. Van Laethem, J-M Josset (Editions Dunod)
- Management : L’essentiel des concepts et pratiques – S. Robbins, D. DeCenzo, M. Coulter, C-C Rüling (Editions Pearson)

Details
- Course title: Design Patterns
- Number of ECTS: 4
- Course code: BPINFOR-88
- Module(s): Module 5.2 * N.C.
- Language: EN
- Mandatory: No
Objectives

The course introduces students to the concepts and the best practices of software engineering centred on Design Patterns. Several individual Design Patterns and some of their possible combinations will be discussed in detail from both theoretical and practical points of view, further familiarizing students with the underlying ideas. The course focuses on the Java programming language in examples and for implementation work.
Course learning outcomes
On successful completion of the course, students are capable to:
- Explain best practices of software engineering centred on Design Patterns.
- Apply several individual design patterns and their combination in practice.
- Explain the underlying ideas of specific design patterns.
Description
In the lectures, the following Design Patterns are explained, grouped with respect to usage themes:
- Theme #1: Creation of objects
  - Singleton.
  - Flyweight.
  - Prototype.
  - Builder.
  - Factory method.
- Theme #2: Hierarchical structures
  - Composite.
  - Visitor.
- Theme #3: Events and notifications
  - Observer.
  - Chain of responsibility.
- Theme #4: Coordinating objects at run time
  - Command.
  - Mediator.
  - Strategy.
- Theme #5: Connecting objects
  - Facade.
  - Adapter.
  - Proxy..
Assessment
- First time students: Continuous assessment during the practical sessions (30%) and final written examination (70%).
- Repeating students: final written examination (100%).
Note
Literature:
- “Design patterns: elements of reusable object-oriented software”, E. Gamma, R. Helm, R. Johnson J. Vissides, Addison-Wesley.
- “Design Patterns for dummies”, Steve Holzner, Wiley Publishing.
- “Design Patterns pour Java”, Laurent Debrauwer, ENI.
- “Design Patterns in Java”, Steven J. Metsker William C. Wake, Addison-Wesley.
- “Head First Design Patterns”, Eric Freeman Elisabeth Freeman, O’Reilly, 2004.
- “Object-Oriented Software Construction”, Bertrand Meyer, Prentice Hall. [version française : “Conception et programmation orientées objet”, Eyrolles].

Details
- Course title: Big Data
- Number of ECTS: 4
- Course code: BPINFOR-82
- Module(s): Module 5.2 * N.C.
- Language: EN
- Mandatory: No
Objectives

The course is about (classical and new) techniques that are involved in the Big Data paradigm. The main goal is to spark the discussion about the Trade Offs between the classical data processing techniques and the upcoming ones for big data. In addition, students should get basic knowledge on how to automatically process and analyze huge amount of data.
Course learning outcomes
On successful completion of this course, students are capable to:
- Demonstrate the ability to understand how to model a database system and the Trade Offs when the database goes big data, such as: consistency versus scalability and performance.
- Explain the database technologies for big data and analyze their pros and cons for proper usage.
- Design and develop big data solutions by adapting existing tools, designing new ones or a combination of both.
- Explain the concepts and the limits of the automated processing of data.
- Differentiate supervised and unsupervised learning and when one technique should be applied.
- Describe the concept of features and the importance of choosing discriminating ones.
- Select among basic algorithms for extracting information from a large data set and apply them.
Description
The course is about (classical and new) techniques that are involved in the Big Data paradigm. The course combines two of the key dimensions of Big Data, namely:
Part I – Design and development for big data
The first part of the course will discuss databases and distributed computing algorithms for hosting and processing very large amounts of data:
1. Conceptual modeling (ER Model), Relational Model (Algebra and SQL), Schema design (ER to Relational).
2. Files and Access methods (except DHT).
3. Distributed Databases, Data Warehouse and C-Store.
4. NewSQL, Distributed Hash Tables, MapReduce, NoSQL.
The main goal of the first part is to spark the discussion about the Trade Offs between the classical data processing techniques and the upcoming ones for big data.
Part II – Data mining, classification and aggregation
The objectives of Part II are to guarantee that the students have a basic knowledge to automatically process and analyze huge amount of data. In particular, the two main objectives are to 1) extract information from a data set and 2) transform and store the data in a convenient way for further use (data mining, classification and aggregation):
- Classification (random forest, …).
- Clustering (k-means, …).
- Outlier and anomaly detection (local outlier factor, …).
- Regression (least squares, …).
Assessment

First time students: Practical exercises, homework, continuous evaluation.

Repeating students: written exam (100%).
Note

Literature: Relevant literature will be provided during the course.

Details
- Course title: Business Software Systems
- Number of ECTS: 4
- Course code: BPINFOR-55
- Module(s): Module 5.2 * N.C.
- Language: EN
- Mandatory: No
Objectives

The students learn an overview and advantages of business software systems, especially ERP systems, and where these systems are used in business.
Course learning outcomes
On successful completion of this course, students are capable to:
- Explain the role of business software systems in professional environments.
- Understand the purpose of business software systems, especially enterprise resource planning (ERP) systems.
- Clarify the role of business processes and apply basic techniques about modeling of business processes.
- Know the core business processes supported by business software systems.
- Understand the term Business Intelligence (BI) and know the components of a business intelligence system.
Description
Overview of business software systems:
- Introduction: business software systems, especially ERP-Systems, as basis of enterprise information processing.
- Classification of business software systems.
- History, market review of ERP systems.
- Architectures of ERP systems (client-server-architectures, layer models, interfaces).
Business Process Management (BPM) and its relation to business software systems:
- BPM cycle.
- Modeling Techniques: BPMN, ARIS.
Main business processes and their support by business software systems:
- Finance and accounting.
- Manufacturing.
- Supply Chain Management.
- Customer Relationship Management.
Management information systems / Business Intelligence:
Case studies are analyzed in the class and assignments with practical exercises with BPM tools are given to the students.
Assessment
- First time students: written examination (70%) and control of homework/assignments (30%).
- Repeating students: written examination (100%).
Note
Literature:
- Kenneth Laudon, Jane P. Laudon: Management Information Systems: Global Edition, 13th ed., Pearson Education, 2014.
- K. Ganesh et. al.: Enterprise Resource Planning – Fundamentals of Design and Implementation, Springer International Publishing, 2014.
- M. Dumas, M. La Rosa, J. Mendling, H. A. Reijers: Fundamentals of Business Process Management, 2nd ed., Springer. 2018.
- B. Silver: BPMN Quick and Easy Using Method and Style: Process Mapping Guidelines and Examples Using the Business Process Modeling Standard, Cody-Cassidy Press. 2017

Details
- Course title: Circuits numériques
- Number of ECTS: 4
- Course code: BPINFOR-111
- Module(s): Module 5.2 * N.C.
- Language: FR
- Mandatory: No
Objectives

Conception des circuits intégrés numériques à l’aide du langage VHDL et réalisation d’un petit microprocesseur simple sur un circuit programmable de type FPGA.
Course learning outcomes
Après avoir suivi ce cours, les étudiants seront capables :
- d’appliquer langage VHDL pour la réalisation d’un petit microprocesseur.
- de programmer un circuit programmable de type FPGA.
Description
- Langage VHDL: définition du langage, concepts de base et méthodologie.
- Logiques combinatoires et séquentielles.
- Automates à états finis et modes de synchronisation.
- Définition et spécification d’un microprocesseur basique.
- Architecture de Von Neuman, constituants d’un CPU et jeu d’instructions ALU et arithmétique des ordinateurs.
- Pipeline, architecture de Harvard et RISC.
- Améliorations successives apportées à l’architecture de base.
Assessment

Un examen écrit ou oral (50%), rapport et présentation orale du projet réalisé en TP (50%).
Note

Les étudiants devront obligatoirement participer aux TP et remettre les rapports qui leur sont demandés.

Details
- Course title: Cloud-Based Applications
- Number of ECTS: 4
- Course code: BPINFOR-116
- Module(s): Module 5.2 * N.C.
- Language: EN
- Mandatory: No
Objectives

The course provides an introduction to both the theoretical foundations and practical applications concerning the broad area of “Cloud Computing”.

The course covers both the practical development and deployment of cloud-based computing applications as well as current tools and respective application-programming interfaces (APIs) in the context of the Apache Hadoop and Spark ecosystems (cloud-based “big data” processing).
Course learning outcomes
On successful completion of this course, students are capable to:
- Explain both the theoretical foundations and the practical usage of current cloud-computing architectures.
- Explain concepts on virtualization and application deployment into the cloud.
- Describe how different concepts concerning the modeling and management of large data collections are implemented on top of these architectures.
- Prove first-hand experience about usage of the introduced tools in AWS / Google Cloud.
Description
The course will cover two important aspects of cloud-based applications, compute-centric and data-centric applications, in particular the following topics:
- Infrastructures for Cloud Computing.
- Virtualization vs Containers – Docker.
- Container orchestration with Kubernetes.
- Practical Cloud examples: AWS, Google Cloud, RedHat OpenShift.
- Distributed file systems (GFS & HDFS).
- Distributed computing principles (MapReduce), replication, fault tolerance, backup tasks, custom combiners and partitioners, local aggregation, linear scalability.
- Apache Pig: first dataflow language (Pig Latin), translation into MapReduce and optimization.
- Apache HBase: distributed key-value store for very large tabular data, columns and column families, indexing and lookups.
- Apache Hive: SQL-like query language on top of Hadoop, translation into MapReduce.
- MongoDB: API overview, JSON processing, user-defined functions.
- Apache Spark: distributed resilient data objects (RDDs), overview of streaming and machine-learning extensions.
- An introduction to High Performance Computing (HPC).
Assessment
- First time students: 2 graded exercises (15% each) and final written exam (70%).
- Repeating students: final written exam (100%).
Note

Literature: The course will rely on genuine documentation available on the web, which will be made available on the course Moodle website.

Details
- Course title: Introduction to Machine Learning
- Number of ECTS: 4
- Course code: BPINFOR-113
- Module(s): Module 5.2 * N.C.
- Language: EN
- Mandatory: No
Objectives

This course introduces Machine Learning (ML) principles and its three main learning paradigms (supervised, unsupervised, and reinforcement learning). For each learning paradigm, it presents some of its most typical foundational models and discuss them from the perspective of representation, evaluation, and optimization. A special attention is given to a basic introduction into deep learning techniques and generalization. The course mixes theoretical concepts with vanilla implementations of various ML models.
Course learning outcomes
At the end of the course the student will be able to:
- Define Machine Learning and differentiate the terms AI, Machine Learning, and Deep Learning.
- Describe the differences among the three Machine Learning paradigms: supervised, unsupervised, and reinforcement learning.
- Determine which of the three Machine Learning paradigms is appropriate to a particular type of problems.
- Derive, implement, and evaluate some of the most typical Machine Learning models.
- Derive, implement, and evaluate some basic Deep Learning models and their learning algorithms.
- Explain proper ML evaluation procedures, including the differences between training and testing performance.
- Apply Machine Learning models to real-world problems.
- Identify overfitting in the context of a problem and describe solutions to overfitting.
- Evaluate the performance of a Machine Learning algorithm on a real-world dataset.
Description
The following topics are covered in the course:
- Basics – ML Introduction.
- Basics – Data preparation for ML.
- Supervised Learning – Regression.
- Supervised Learning – Classification.
- Unsupervised Learning – Dimensionality reduction.
- Unsupervised Learning – Clustering.
- Reinforcement Learning – Preliminaries.
- Reinforcement Learning – Basic methods.
- Deep Learning – Learning Deep Representations.
- Deep Learning – Models.
- Deep Learning – Deep Reinforcement Learning.
- Generalization.
- Research – Glimpse on state-of-the-art research.
- Engineering – ML and the real-world.
Assessment
- First time students: two take-home assignments for 10% and one take-home assignment for 20% of the final grade, written exam: 60% of the final grade, open-book exam (i.e., course material allowed).
- Repeating students: final written exam (100%).
Note
Literature:
- Bishop, C.: Pattern Recognition and Machine Learning, 2006
- Goodfellow, I., Bengio, Y. Courville, A.: Deep Learning, 2016
- Sutton, R., Barto, A.: Reinforcement Learning: An Introduction, second edition, 2018
- Hastie, T, Tibshirani, R. Friedman, J.: The Elements of Statistical Learning, 2009
- Zhang, A., Smola, A.J., Lipton, Z., Li, M.: Dive into Deep Learning, 2023

Details
- Course title: Introduction to IT Security
- Number of ECTS: 4
- Course code: BPINFOR-51
- Module(s): Module 5.2 * N.C.
- Language: EN
- Mandatory: No
Objectives
The general goal of the course is to increase awareness for IT security. The course explains a broad range of common algorithms and techniques used in IT security including IT security management.

By the end of the course, the students will have:
- A good understanding of the role of security and the importance of security in IT systems.
- A good understanding of security properties and the mechanisms used to enforce such properties.
- Familiarity with techniques for modelling and evaluating secure systems against given security requirements.
- A vision of the management of the IT securities and the related regulatory that we need to manage as IT responsible of a firm.
Course learning outcomes
On successful completion of this course, students are capable to:
- Explain the role of security and the importance of security in IT systems.
- Describe security properties and the mechanisms used to enforce such properties.
- Apply techniques for modelling and evaluating secure systems against given security requirements.
- Describe common techniques used for the management of IT security.
Description
Introduction – IT security concepts:
1. Overview.
2. Key concepts and definitions (confidentiality, integrity, authentication, privacy, security dilemmas).
First part:
1. Introduction to basic cryptographic tools (symmetric/asymmetric cryptography, hash functions, signatures).
2. Identification and authentication.
3. Access control and security models: Authorization, policies and enforcement mechanisms.
4. Database and datacenter securities and kind of attacks:
  1. Malicious software.
  2. Trojans.
  3. DOS.
5. Intrusion detection and firewalls.
6. Software and Network securities and Trusted systems:
  1. Web security.
  2. Email security.
  3. Security protocols.
Assessment
- First time students: written exam (60%) and practical exercises (40%).
- Repeating students: written exam (100%).
Note
Literature:
- W. Stallings, L. Brown: Computer Security Principles and Practice, Pearson, 4th edition, 2018, ISBN: 978-0-13479435-8.
- B. Schneier: Applied Cryptography: Protocols, Algorithms, and Source Code in C, Wiley, 2nd edition, 1996, ISBN: 978-1-119-09672-6 .

Details
- Course title: Java for Enterprise Applications
- Number of ECTS: 4
- Course code: BPINFOR-110
- Module(s): Module 5.2 * N.C.
- Language: EN
- Mandatory: No
Objectives

Students will learn how Jakarta EE (formerly Java EE) offers a component-based approach to designing, developing, assembling, and deploying enterprise applications. Both theoretical and practical aspects of Jakarta EE components will be explored, with particular emphasis on the role of web services in enabling service-oriented architectures. Hands-on experience will be gained using WildFly, a free Jakarta EE application server closely related to the popular commercial JBoss server. In the second part, students will receive a practical comprehensive introduction to the competitive Spring Boot framework.
Course learning outcomes
On successful completion of this course, students are capable to:
- Explain the main ideas behind the Jakarta EE framework.
- Develop views with the help of JSF and named beans.
- Explain the practical significance of the most important APIs in the framework.
- Develop Jakarta EE-based applications of medium-level difficulty with the application server Wildfly.
- Implement web services and web applications with Spring Boot.
- Extend their knowledge on Jakarta EE and Spring Boot by autonomously reading the specifications and other documentation available.
Description
- Introduction to the Docker containerization framework and its usage in this course.
- General Jakarta EE concepts.
- Traditional Java-based web applications: Java Servlets / Java Server Pages (JSP) / Custom tags / JSTL.
- Java Server Faces and Named Beans as view technology in Jakarta EE.
- Session Beans: stateless, stateful, and singleton beans.
- Context Dependency Injection (CDI) and its benefits.
- The Java Persistence API.
- Dedicated APIs of the Jakarta EE framework (XML, JSON, batch jobs, …).
- The Java Message Service (JMS) and Message-Driven Beans.
- Develop web services with Jakarta EE.
- Introduction to Jakarta EE Security.
- Introduction to Spring Boot, especially REST web services, Spring Boot persistence, and MVC web applications with Spring Boot.
- Spring Boot with relational databases (MariaDB) and NOSQL databases (MongoDB, Redis)
- Spring Boot and GraphQL.
- Concepts for Spring Boot Security (Spring Boot app combined with a KeyCloak authentication server)
- Native enterprise applications with Spring Native, Quarkus, and Micronaut.
- Comparison of advantages and disadvantages of Jakarta EE and Spring Boot.
Assessment
- First time students: two graded assignments (30%) and final written exam (70%)
- Repeating students: final written exam (100%).
Note

Literature: The course mainly uses the various Jakarta EE component specifications available online. A detailed list of references is made available on the Moodle course website.

Details
- Course title: Parallel and Distributed Systems
- Number of ECTS: 4
- Course code: BPINFOR-117
- Module(s): Module 5.2 * N.C.
- Language: EN
- Mandatory: No
Objectives

Many applications and systems extend beyond the boundaries of a single process, often being deployed on multiple machines. The main objective is to provide a solid understanding of the theoretical background and general principles on how to compose a single coherent system composed of multiple distributed components. Modern system architectures are presented and discussed, including cloud computing, peer-to-peer, grid, ad-hoc and mobile systems. Diverse issues in terms of the design and implementation of such systems are explored, with a strong emphasis on practical aspects, including performance. Real-world distributed systems are examined as case studies.
Course learning outcomes
On successful completion of this course, students are capable to:
- Prove a solid understanding of the general principles used in distributed systems.
- Explain the usage of distributed systems in real-world situations.
- Practically realize a distributed system.
Description
- Distributed system architectures.
- Time in distributed systems.
- Distributed processes.
- Interprocess communication.
- Distributed synchronisation.
- Fault tolerance (reliability, replication).
- Peer-to-peer systems.
- Grid computing.
- RPC, RDMA, MPI.
- Real-time streaming storage and processing.
- Partitioning, Scaling, Performance.
- Latency, Throughput.
Assessment
First time students:
- 40%: In-class practical labs: Four labs are designed to directly apply the concepts learned from both traditional lectures and pre-class materials. Evaluation is based on successful implementation, problem-solving approach, and demonstrated understanding during the session.
- 20%: Quizzes with written explanations: Four quizzes will assess understanding of key concepts from both lectures and preparatory materials. To evaluate depth of knowledge, students will be required to provide clear written explanations to justify their answers, demonstrating their reasoning process.
- 30%: Final written exam (mandatory).
- 10%: Active engagement in class.
Repeating students: 100% final written exam.
Note
Literature:
- Distributed Systems, Tanenbaum as optional reading.
- Lecture notes and papers are based on MIT’s 6.824

Details
- Course title: Backend Software Development
- Number of ECTS: 4
- Course code: BPINFOR-122
- Module(s): Module 5.2 * N.C.
- Language: EN
- Mandatory: No
Objectives

This course covers advanced topics in back-end Web development, including API design, implementation using different languages and frameworks, and integration of databases. Proper use of asynchronous programming together with concurrency handling to tackle scalability is discussed extensively based on several case studies.
Course learning outcomes
On successful completion of this course, students are capable to:
- Assess Web development technologies and frameworks.
- Design and develop scalable back-ends for Web apps.
- Properly use asynchronous programming while dealing with concurrency issues.
Description
The course covers the following topics using a hands-on approach:
- Basics:
- Asynchronous and reactive programming.
- RESTful API design and use.
- OpenAPI.
- GraphQL.
- Interaction between front- and back-end.
- JavaScript-based Web development:
- Node.js.
- Express.
- Python-based Web development:
- Flask.
- Databases and persistence:
- MySQL.
- MongoDB.
Assessment
Winter Semester:
- First time students: continuous evaluation (30%), three closed-book written exams (70%).
- Repeating students: closed-book written final exam (100%).
Summer Semester:
- Closed-book written final exam (100%)
Note

Literature: References will be announced in the class.

Details
- Course title: Introduction to IOT
- Number of ECTS: 4
- Course code: BPINFOR-124
- Module(s): Module 5.2 * N.C.
- Language: EN
- Mandatory: No
Objectives
Students will be equipped with competences for the development of applications for the Internet of Things (IOT):
- Know the challenges of IoT networks considering the various specificities and application constraints (e.g. QoS, scalability, real-time application, operational safety, etc.).
- Master the basics of IoT communication protocols (Application layer).
- Master the selection and implementation of different IoT communication protocols.
- Design, analyze and criticize different data collection, storage and processing data architectures, both their possibilities and their limits.
- Design one or more IoT applications using machine learning modules based on the collected data.
Course learning outcomes
On successful completion of this course, students are capable to:
- Define and develop the functional and technical specifications of a network and telecom equipment (hardware, software, implementation, etc.).
- Determine architecture components, technologies, equipment, tools supports and integrate them according to the specifications.
Description
- Introduction to issues relating to the interoperability of IOT networks / protocols (application layer – OSI).
- Real-time networks: Profibus, Modbus, Modbus-TCP.
- IOT protocols on application layer: HTTP (REST API), MQTT, CoAP, OneM2M, O-MI/O-DF.
- NOSQL databases: MongoDB, ElasticSearch.
- Getting started with Node-Red (visual programming tool – open source – developed by IBM) for IoT application development:
  - data collection: Arduino, & sensors, Cloud API endpoints…
  - data storage: databases (SQL, NoSQL).
  - data treatment: Node-Red (JavaScript).
  - publication of data via dashboard (H2M) and machine interfaces (M2M): Implementation of an HTTP server (REST API specification, server deployment).
Assessment
- First time students:
  - 40%: practical group project.
  - 60%: final written exam.
- Repeating students: 100% final written exam.
Note

This course is shared with the Bachelor in Computer Science (BiCS).Literature: Relevant literature will be provided during the lecture.

Details
- Course title: IT in a Real organization
- Number of ECTS: 4
- Course code: BPINFOR-126
- Module(s): Module 5.2 * N.C.
- Language: EN
- Mandatory: No
Objectives

The students will learn how IT is structured, driven and operated in a real financial organization – based on an example of a bank in Luxembourg. The course will cover different IT job families, IT project governance and lifecycle, practical hints for system design, insights into the hiring and job onboarding process.
Course learning outcomes
On successful completion of this course, students are capable to:
- Understand the place, the role, the priorities and the driving factors of IT in a real organization.
- Differentiate between different IT job families.
- Understand how IT projects are structured and executed.
- Implement practical system designs.
- Navigate through the hiring and job onboarding process.
Description
- A structure of a typical bank in Luxembourg and a place of IT in it.
- IT job families – from a software engineer to quality assurance, system analysis, user experience and project management.
- IT project governance and project execution.
- System design – DOs and DON’Ts.
- Hiring process – job search, interview preparation, interview types (live coding, brain teaser, tech vs HR), interview follow-up, first days at job.
Assessment
First time students:
- End-course evaluation lab teamwork (30%): Design and build a demo system
  - Structure project: scope, governance, timelines, planning.
  - Execution phases: launch, planning, analysis, design, implementation, deployment, landing.
  - Project roles and their responsibilities: lead, analyst, engineers etc.
- Individual student presentation (30%).
- Final written exam (30%).
- Participation in course (10%).
Note

Literature: Lecture slides and supporting materials will be provided on the Moodle course website.

Details
- Course title: Digital transformation
- Number of ECTS: 4
- Course code: BPINFOR-131
- Module(s): Module 5.2 * N.C.
- Language: EN
- Mandatory: No
Objectives
1. Historical Context: Introduce the evolution of the digital revolution and its economic and societal impacts.
2. Technological Understanding: Provide in-depth insights into key technologies driving digital transformation (AI, Blockchain, etc.).
3. Strategic Application: Develop the ability to design and evaluate digital transformation initiatives for businesses.
4. Futuristic Outlook: Explore the next wave of digital technologies (Quantum Computing, AGI) and analyze potential risks for humanity.
5. Practical Experience: Enable students to apply concepts through a final project that addresses real organizational challenges.
Course learning outcomes
By the end of this course, students will be able to:
- Analyze the historical progression of digital technologies and articulate their influence on business models.
- Evaluate how data and emerging technologies can optimize and transform organizational processes.
- Design a digital transformation strategy aligned with business objectives, leveraging relevant tools.
- Critically Assess ethical, social, and human risks associated with advanced technologies.
- Collaborate effectively in teams to produce a comprehensive written report and deliver an engaging presentation on a proposed digital transformation plan.
Description

This course provides a comprehensive exploration of digital transformation, centering on the technologies and strategic approaches that organizations adopt to thrive in today’s evolving digital landscape. Students begin by examining the origins of the digital revolution to understand the economic and societal shifts that paved the way for current innovations. From there, the curriculum delves into how data-driven methodologies revolutionize business processes, enabling greater efficiency and competitive advantage.
Key technologies such as Artificial Intelligence (AI) and Blockchain will be discussed in depth, illustrating how they are applied across various industries. The course also highlights emerging fields like Quantum Computing and Artificial General Intelligence (AGI), considering both their transformative potential and associated risks. Real-world case studies, featuring both successful and failed digital transformation strategies—will demonstrate the importance of sound planning, change management, and stakeholder engagement.
A guest speaker from industry will offer practical insights, sharing lessons learned from an ongoing digital transformation journey. Building on all these insights, students will collaborate on a final group project where they propose a comprehensive digital transformation strategy for a real or fictional company, integrating the technological and managerial perspectives covered in the class.
Assessment
Final Project (50%): Group Work (2 – 3 students)
- Written Report: A thorough digital transformation strategy for a chosen real or fictional company
- Presentation: 10 – 15 minutes in class, followed by QA
Multiple Choice Exam (50%): Covering the topics seen in class (closed book, paper pencil)
Note
Literature:
- The Digital Transformation Roadmap: Rebuild Your Organization for Continuous Change, David L. Rogers, 2023, ISBN 978-0231196581
- Digital Transformation: Survive and Thrive in an Era of Mass Extinction, Thomas M. Siebel, 2019, ISBN 978-0231196581
- The Economics of Data, Analytics, and Digital Transformation: The theorems, laws, and empowerments to guide your organization’s digital transformation, Bill Schmarzo, 2020, ISBN 978-1800561410

Details
- Course title: Introduction to Generative AI
- Number of ECTS: 4
- Course code: BPINFOR-132
- Module(s): Module 5.2 * N.C.
- Language: EN
- Mandatory: No
Objectives

This course introduces students to the field of Generative AI and LLM-based (Large Language Model) agents. The course will explain key concepts such as neural networks, transformers, RAG (Retrieval-Augmented Generation) systems, diffusion models, agentic AI and how to build and evaluate generative AI applications responsibly. Students will be guided through hands-on practical sessions using state-of-the-art tools and frameworks (e.g., PyTorch, Langchain, vector databases) and will learn best practices for designing, training, tuning, and deploying large-scale AI systems.
Course learning outcomes
At the end of the course, students will be able to:
- Understand the history, current impact, and real-world applications of Generative AI and LLM-based agents.
- Understand core concepts of NLP, including embeddings, vector representations, semantic similarities, and key ML paradigms (supervised, unsupervised, reinforcement learning).
- Describe how neural networks are trained, including essential knowledge of PyTorch (tensor ops, autograd).
- Demonstrate proficiency with transformers (attention, tokenization, pruning, distillation) for language tasks.
- Engineer effective prompts for generative models, mitigating limitations and hallucinations.
- Build, fine-tune, and evaluate RAG systems using vector databases.
- Fine-tune diffusion models and GANs for generative tasks.
- Analyze ethical, socio-economic, and security implications of deploying and maintaining GenAI solutions.
- Apply best practices when creating LLM-based agents with reasoning and tool usage.
- Evaluate GenAI/ML models using standardized metrics, procedures, and datasets.
Description
- Introduction to Generative AI, LLMs, and course structure.
- Python basics and AI environment setup.
- Key machine learning concepts and data importance.
- Intro to PyTorch: tensors, autograd, and GPU use.
- Neural networks and training fundamentals.
- Building and training a basic neural network in PyTorch.
- NLP basics and word embeddings (word2vec, GloVe).
- Prompt engineering with OLLAMA and handling limitations.
- Transformer architecture and model optimization.
- Building a RAG (Retrieval-Augmented Generation) system.
- Fine tuning vs. instruction tuning of LLMs.
- Running a basic fine-tuning job on a pre-trained LLM.
- Generative models: GANs and diffusion models.
- Tuning parameters in a Stable Diffusion demo.
- Lifecycle of a generative AI project and cost trade-offs.
- Building a chatbot with LangChain (hands-on).
- Understanding LLM-based agents and tool use.
- Advanced prompt engineering and reasoning workflows.
- Evaluation metrics for generative AI models.
- Hands-on model evaluation and monitoring in production.
- Ethics and socio-economic impact of generative AI.
- Security in GenAI: prompt injection and jailbreaking (Part I).
- GenAI threats and deployment hardening strategies (Part II).
- Practical security lab: securing an LLM-based system.
- Real-world GenAI case studies across industries.
- Mini-project: advanced RAG and agent integration.
- Future of AI, AGI, and open research problems.
- Final project presentations and peer feedback.
Assessment
- First time students:
  - Take-home assignment: Implement a mini GenAI application (e.g. chatbot, RAG system, …) and provide a concise report documentation design, approach, and evaluation (40%).
  - Final written exam (60%).
  - Active participation (up to 2 bonus points).
- Repeating students: final written exam (100%).
Note

Literature: Will be announced during the course via Moodle.