Artificial Intelligence, Systems, Data - Mathematics Track - Master's Year 2

Syllabus

Cours introductifs obligatoires

  • A review of probability theory foundations

UE fondamentales 3

  • Bayesian statistics
  • Data Science Lab
  • Foundations of Machine Learning
  • High-dimensional statistics
  • Optimal transport
  • Optimization for Machine Learning
  • Reinforcement learning

UE optionnelles (5 UE à choisir)

  • Advanced machine learning
  • Bayesian case studies
  • Bayesian machine learning
  • Computational social choice
  • Computational statistics methods and MCMC
  • Dimension reduction and manifold learning
  • Data acquisition, extraction and storage
  • Deep learning for image analysis
  • Graph analytics
  • Incremental learning, game theory and applications
  • Introduction to causal inference
  • Knowledge graphs, description logics, reasoning on data
  • Large language models
  • 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
  • Point cloud and 3D modelling
  • Topics in trustworthy machine learning

PSL Week - 2 ECTS

Bloc stage - 10 ECTS

  • Stage

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 Computer Science and Mathematics tracks 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 th e first semester.


Internships and Supervised Projects

The students must complete a 5-month internship, starting in April.

For students : how to find an internship, and obtain the agreement ?
To find an internship, you can consult the list of internship offers, or approach the laboratories or companies that interest you yourself. Next, you'll need to obtain pedagogical approval for your internship subject. To obtain it, upload your subject here. Specify in the comments that the subject is for you. (Please do not send your subject by e-mail). Once you have obtained pedagogical validation, you can fill in the form in the ESUPstage application to obtain your internship agreement. For more information on the presentation of internships.

For supervisors : how to offer an internship to Master IASD - Computer science track Master's students ? If you are part of a research laboratory or R&D department, you can propose an internship topic to students by clicking here. Of course, the internship must be related to one of the subjects covered in the Master's program. The internship will appear in the list below once it has been validated by the teaching staff.

Gratification : in France, internships lasting more than 2 months must be gratified. Use the tool for calculating gratification for interns.