Artificial Intelligence, Systems, Data (IASD) - Master's Year 2

Program Year

UE obligatoires

  • Fondamentaux de l’apprentissage automatique
  • Optimization for Machine Learning
  • Bases de données avancées (SBGD non classiques)
  • Représentation des connaissances, raisonnement, planification
  • Projet Sciences des Données
  • Apprentissage Profond

UE optionnels

  • Apprentissage automatique avancé
  • Apprentissage incrémental, Théorie des Jeux et Applications
  • Apprentissage profond pour l’analyse d’images
  • Traitement Automatique des Langues
  • Nuages de Points et Modélisation 3D
  • Ethique et intelligence artificielle
  • Graphes de connaissance, logiques de description, raisonnement sur les données
  • Fouille de Graphes
  • Machine Learning sur Big Data
  • Choix social computationnel
  • Recherche Monte-Carlo et Jeux
  • Introduction to reinforcement learning
  • Deep renforcement learning et applications
  • Anonymisation, confidentialité
  • Data wrangling, qualité de données
  • Fondements des langages de requête graphe et RDF
  • Semaine intensive PSL Humanité numérique
  • Semaine intensive PSL Géonomique
  • Module d'ouverture

UE Obligatoires

Academic Training Year 2022 - 2023 - subject to modification

Teaching Modalities

The program starts in September and attendance is required.
The year consists of a semester of advanced instruction in AI (from September to December) followed by a semester of electives (from January to April) and a research internship (April to September). Courses are divided over two semesters. During the first core semester, from September to Decembers, students must take six courses in artificial intelligence and data science, for a total of 30 ECTS credits. During the second, elective semester, from January to April, students must choose at least six advanced courses from a wide range of options (advanced machine learning, natural language processing, scatter plots and 3D modeling, ethics and data science, data mining, etc.) for a total of 18 ECTS credits.
For students who need it, a refresher course in foundations of mathematics and computer science is offered before the core semester begins in September.

UE obligatoires

  • Fondamentaux de l’apprentissage automatique
  • Optimisation pour l'apprentissage automatique
  • Bases de données avancées (SBGD non classiques)
  • Apprentissage Profond
  • Systèmes, paradigmes et langages pour les Big Data
  • Ethique et science des données
  • Fouille de Graphes
  • Data wrangling, qualité de données
  • Traitement automatique des langues - NLP
  • Apprentissage par renforcement

UE obligatoires

  • Apprentissage profond pour l’analyse d’images
  • Flux de données
  • Recherche Monte-Carlo et Jeux
  • Visualisation de données
  • IA sur le Cloud
  • Graphes de connaissance, logiques de description, raisonnement sur les données
  • Machine Learning sur Big Data
  • Projet Sciences des Données

UE obligatoires

Academic Training Year 2022 - 2023 - subject to modification

Teaching Modalities

The program starts in September and attendance is required. Students rotate between four weeks at a company and two or three weeks at the university.

The program is divided into two semesters, S3 and S4. Each semester is made up of a course, as well as a thesis in S4.
The final grade for a course is the cumulation of grades for continuous assessment, projects, homework, oral or written exams, and participation. Every course for which a student receives a final grade of 10/20 or above is deemed passed, and the appropriate ECTS credits are granted.

Each semester is made up of courses, as well as a thesis in S4. A student will have passed a semester if all the following conditions are met:

  • They take at least 30 ECTS Their final grade for the semester is at least 10/20
  • Their final grade for the semester is at least 10/20.
  • The final grade for each course taken that semester is at least 6/20
  • The final grade for the thesis in semester 4 is at least 10/20
  • If a student has passed a semester, they are to considered to have passed all the courses that make up that semester and to have received the associated ECTS credits.

To pass a year, the student must have passed both semesters and all the courses that make up that unit and to have received the associated ECTS credits. A student will have passed a year (and received the associated 60 ECTS credits) if all the following conditions are met:

  • They have taken at least 60 ECTS credits and receive a final grade for the year of at least 10/20
  • The final grade for each semester is at least 10/20
  • The final grade for each course in each semester is at least 6/20
  • The final grade for the thesis in semester 4 is at least 10/20


Internships and Supervised Projects

Students intern in an academic or private research center from April to August and then write and defend a thesis in September.