Artificial Intelligence, Systems, Data (IASD) - Computer science track - Master's Year 2
Syllabus
UE fondamentales 3
UE optionnelles (6 UE à choisir)
- Advanced machine learning
- Bayesian case studies
- Bayesian machine learning
- Bayesian statistics
- Computational social choice
- Computational statistics methods and Markov chain and Monte-Carlo methods
- Dimension reduction and manifold learning
- Dynamical systems in machine learning
- Evolution and online learning in games
- Graph analytics
- High dimensional statistics
- 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
- Optimal transport
- Point clouds and 3D modeling
- Robot learning
- Science des données
- Trustworthy machine learning
PSL Week - 2 ECTS
Bloc stage - 10 ECTS
Academic Training Year 2026 - 2027 - 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 degree consists of a common core semester on the fundamental disciplines of AI (from September to December; 6 mandatory courses, equivalent to 144 hours – 24 ECTS) followed by a semester of options (from January to March; 7 optional courses, equivalent to 164 hours – 26 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 six 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 three 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.
Research Support
Research-driven Programs
Training courses are developed in close collaboration with Dauphine's world-class research programs, which ensure high standards and innovation.
Research is organized around 6 disciplines all centered on the sciences of organizations and decision making.
Learn more about research at Dauphine
