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

Syllabus

UE fondamentales 3

  • Data acquisition, extraction and storage
  • Data Science Lab
  • Deep learning for image analysis
  • Foundations of Machine Learning
  • Large language models
  • Optimization for Machine Learning
  • Reinforcement learning

UE optionnelles (5 UE à choisir)

  • Advanced machine learning
  • Bayesian case studies
  • Bayesian machine learning
  • Bayesian statistics
  • Computational social choice
  • Computational statistics methods and MCMC
  • Dimension reduction and manifold learning
  • Graph analytics
  • High-dimensional statistics
  • Incremental learning, game theory and applications
  • Introduction to causal inference
  • Knowledge graphs, description logics, reasoning on data
  • LLM for code and proof
  • Machine learning on Big Data
  • Machine learning with kernel method
  • Mathematics of deep learning
  • Monte-Carlo search and games
  • Non-convex inverse problems
  • NoSQL databases
  • Optimal transport
  • Point cloud and 3D modelling
  • Topics in trustworthy machine learning

PSL Week - 2 ECTS

Bloc stage - 10 ECTS

Academic Training Year 2025 - 2026 - subject to modification

Teaching modalities

Courses are held at 16 bis rue de l'Estrapade, 75005 Paris.

Detailed assessment methods are communicated at the beginning of the year.

The IASD Master’s program begins with a core semester devoted to the fundamental disciplines of AI and data science, consitsing of four common courses and three courses specific to the two tracks, Computer Science and Mathematics. At the end of the first semester, students choose six additional courses for the second semester, including the opportunity to follow an intensive PSL week, allowing them to open up to other disciplines or applications. The year continues with an internship in an academic or industrial research laboratory, ending in September with the writing of the master thesis and its public defense.

The IASD Master’s degree consists of a common core semester on the fundamental disciplines of AI (from September to December; 7 mandatory courses, equivalent to 168 hours – 28 ECTS) followed by a semester of options (from January to March; 6 optional courses, equivalent to 140 hours – 22 ECTS) and an internship (from April to September; 10 ECTS) done in an academic research lab or an R&D company. The common core semester includes seven mandatory courses, while the second semester allows students to deepen their knowledge in six subjects chosen from twenty options. Students also have the opportunity to attend an intensive PSL week proposed by the DATA program at Université PSL. Optional refresher courses on probability and programming foundations are offered before the start of the common core courses in early September.
The IASD - Computer Science track and IASD - Mathematics track share four common courses in the first semester, while three other courses are specific to each track. Courses specific to the other track may also be followed as option(s), in the limit of two options (at most) to be followed during the first semester.