Code
MGFE MIS 4404
Niveau
M1
Discipline
Systèmes d’information
Langue
Anglais/English
Crédits ECTS
2
Heures programmées
18
Charge totale étudiant
40
Coordonnateur(s)
Département
- Data analytics, Économie et Finances
Equipe pédagogique
Introduction au module
This course builds on the data skills developed in the Introduction to Programming course to explore how artificial intelligence works, what it can and cannot do, and how to deploy it responsibly in business contexts.
Rather than surveying AI as a collection of techniques, this course asks a more fundamental question: what problem are we trying to solve, and is AI the right tool to solve it? Starting from how humans reason and make decisions, we examine how machines can be designed to do the same — through search, optimization, learning from data, and generating new content. We cover the major paradigms of AI including supervised and unsupervised machine learning, neural networks, and large language models, grounding each in concrete business applications across marketing, finance, operations, and beyond.
The course follows a project-based format: short concept sessions are immediately followed by hands-on work, and all technical content is applied to a real dataset your team has already been working with since the Introduction to Programming course. The final deliverable is an AI advisory brief — combining model results with a structured business recommendation — presented to the class on the last day.
Ethics, regulation, and the responsible use of AI are not an afterthought: they are woven throughout the course and form part of the final evaluation.
This course requires a personal computer (tablets are not recommended), active participation throughout, and a willingness to work with code, data, and ideas simultaneously. Students who arrive prepared and engaged will leave with both the technical fluency and the critical judgment to navigate AI in their professional lives.
Bloc de compétences
- 6. Concevoir et/ou piloter des solutions de gestion innovantes en veillant à garantir une création de valeur soutenable pour toutes les parties prenantes
Compétences du bloc
- 6.1 - Concevoir, développer et appliquer des politiques et pratiques propices au dynamisme de l'organisation, pour résoudre des problématiques repérées, en intégrant les spécificités du contexte métier.
Objectifs d'apprentissage du cours
At the end of this PGE2(M1) course, each student will be able to:
1. Describe the major paradigms of AI — search, optimization, knowledge/uncertainty, and machine learning — and contrast each paradigm's problem-solving logic using a concrete business example.
2. Identify and distinguish between supervised learning, unsupervised learning, neural networks, and large language models, and match each to appropriate business use cases, strengths, and limitations.
3. Given a business problem and dataset, select and justify an appropriate AI/ML approach; define and operationalize what good model performance means in that specific context; and articulate the trade-off between false positives and false negatives for the business decision at stake.
4. Build and interpret a machine learning model on a real dataset; challenge model outputs by testing assumptions, checking for data leakage, and questioning whether results hold under different conditions; and connect findings to the original business question.
5. Apply the AI Transformation Playbook framework to assess the data, talent, infrastructure, and governance requirements needed to deploy an AI solution in a specific organisational context.
6. Evaluate the ethical risks and regulatory implications of an AI solution using a structured framework, and defend a responsible deployment recommendation against counterarguments during the final presentation.
7. Select, prompt, and evaluate AI tools as thinking partners during analysis and advisory work; identify errors or bias in AI-generated outputs; and document the boundary between AI assistance and original team reasoning in the final deliverable
Contenu : structure du module et agenda
This course corresponds to the second module of the DATA CAMP
**Day 1 **: Block 1 — what is AI, history, types, search (content + group practice). Block 2 — knowledge/uncertainty (Bayes), optimization (content + group practice) + project re-orientation: teams revisit their dataset through an AI/ML lens. Block 3 — ML overview + supervised learning: basics (content + group practice + individual MCQ).
**Day 2 (6h)**: Block 1 — supervised learning: trees/forests, LASSO/XGBoost (content + group practice). Block 2 — unsupervised learning (content + group practice). Block 3 — neural networks (content + group practice + individual MCQ).
**Day 3 (3h)**: Block 1 — NLP, LLMs/transformers (content + group practice). Block 2 — ethics (content + group practice) + AI Transformation Playbook (content + group practice).
**Day 4 (3h)**: Block 1 — regulation, compressed (content+discussion) + final project work/polish. Block 2 — presentations + final individual exam
Contribution à l'atteinte des ODD (Objets du Développement Durable)
This course contributes to ODD 4 by developing advanced AI literacy and critical analytical skills, equipping students to navigate and evaluate the increasingly AI-driven environments they will encounter in their professional and civic lives. It contributes to ODD 8 by preparing future managers with the technical fluency and strategic judgment needed to lead data-driven organisations and contribute to inclusive, sustainable economic growth in a labour market undergoing rapid technological transformation. It contributes to ODD 9 by exposing students to the major AI paradigms and their applications across industries, developing their capacity to identify where and how AI can modernise business processes and infrastructures in a responsible and effective way. Finally, it contributes to ODD 16 by integrating AI ethics and regulation — including the EU AI Act and risk management frameworks — into the core of the course, preparing students to design, evaluate, and advocate for AI systems that are transparent, accountable, and aligned with the principles of responsible governance.
Nombre d'ODD abordés parmi les 17
4 ODD
Apprentissage
synchrone
Méthode pédagogique
This course is based on a learning-by-doing approach. The classes will include a theoretical component, which will cover the essential concepts, as well as a practical component where the theory will be applied through programming exercises.
Système de notation et modalités de rattrapage
Continuous assessment (MCQ 2×10% + group project report and presentation 40%): MCQs evaluate students' knowledge of content presented each day. The group project report and presentation are the outcomes of the group challenge students work on during class, applying course concepts to their case study. Individual contributions to the group project are tracked via shared Colab notebook history.
Final exam (40%, paper-based): Given a short business scenario, students reason in writing about the appropriate AI approach for the problem at hand, justify their choice, interpret a provided model output, and articulate the implications of model errors for the business decision at stake. The exam covers the core concepts from the course.
Rattrapage: Only the failed component(s) need to be retaken: a new MCQ, a new individual written exam on the same learning objectives, or a new project based on a different dataset with the same deliverable structure. All rattrapage work is followed by an individual oral exam.
Règlement du module
Professor-Student Communication
● The professor will contact the students through their school email address (IMT-BS/TSP) and the Moodle portal. No communication via personal email addresses will take place. It is the student responsibility to regularly check their IMT-BS/TSP mailbox.
● Students can communicate with the professor by emailing him/her to his institutional address. If necessary, it is possible to meet the professor in his office during office-hours or by appointment.
Students with accommodation needs
If a student has a disability that will prevent from completing the described work or require any kind of accommodation, he may inform the program director (with supporting documents) as soon as possible. Also, students are encouraged to discuss it with the professor.
Class behavior
● Out of courtesy for the professor and classmates, all mobile phones, electronic games or other devices that generate sound should be turned off during class.
● Students should avoid disruptive and disrespectful behavior such as: arriving late, leaving early, careless behavior (e.g. sleeping, reading a non-course material, using vulgar language, over-speaking, eating, drinking, etc.). A warning may be given on the first infraction of these rules. Repeated violators will be penalized and may face expulsion from the class and/or other disciplinary proceedings.
● The tolerated delay is 5 minutes. Attendance will be declared on Moodle during these 5 minutes via a QR code provided by the teacher at each course start.
● Student should arrive on time for exams and other assessments. No one will be allowed to enter the classroom once the first person has finished the exam and left the room. There is absolutely no exception to this rule. No student can continue to take an exam once the time is up. No student may leave the room during an examination unless he / she has finished and handed over all the documents.
● In the case of remote learning, the student must keep his camera on unless instructed otherwise by the professor.
Honor code
IMT-BS is committed to a policy of honesty in the academic community. Conduct that compromises this policy may result in academic and / or disciplinary sanctions. Students must refrain from cheating, lying, plagiarizing and stealing. This includes completing your own original work and giving credit to any other person whose ideas and printed materials (including those from the Internet) are paraphrased or quoted directly. Any student who violates or helps another student violate academic behavior standards will be penalized according to IMT-BS rules.
Références obligatoires et lectures suggérées
Business Data Science, Matt Taddy
Artificial Intelligence: A Modern Approach, Russell, S., & Norvig, P.
Mots-clés
Artificial Intelligence, Big Data, Data Science, Machine Learning
Prérequis
Students are expected to be comfortable working in Python and a Colab environment, including loading and manipulating datasets with Pandas (filtering, groupby, merge), producing basic visualizations, and interpreting descriptive statistics. These skills are covered in the Introduction to Programming course (Part 1 of the Data Camp). Students who have not completed this course must be able to demonstrate equivalent prior knowledge before enrolling