• Mandatory: Refresher in statistics (APM_51438_EP): Marine Le Morvan, Inria
• Optional: Refresher in computer science (CSC_51440_EP): Amal Dev Parakkat, Télécom Paris

All subsequent M1 courses are 36h and will credit 4.5 ECTS.

• Mandatory: Machine Learning (MDC_51006_EP): Erwan Le Pennec and Jesse Reed, École polytechnique
• Elective (at least 1 course):
  • Deep Learning (CSC_51054_EP): Michalis Vazirgiannis and Johannes Lutzeyer, École polytechnique
  • Emerging subjects in machine learning and collaborative learning (APM_51178_EP, research-oriented): Aymeric Dieleveut and El Mahdi El Mhamdi, École polytechnique
  • Signal processing from Fourier to ML (APM_51055_EP): Rémi Flamary, École polytechnique
• Optional:
  • Statistical learning theory (APM_51059_EP): Karim Lounici, École polytechnique
  • Probability theory for ML - Applications to Monte Carlo methods and generative models (APM_51056_EP): Alain Durmus, École polytechnique
  • Image analysis and computer vision (CSC_51073_EP): Mathieu Brédif, Université Gustave Eiffel
  • Topological data analysis (CSC_51056_EP): Steve Oudot, INRIA
• Mandatory non-scientific courses:
  • Introduction to Marketing and Strategy (IME_51456_EP): Philippe Ginier-Gillet, École polytechnique
  • Sport, Humanities, Foreign Language (these courses are similar to those every graduate from École polytechnique must follow)

Students must take one deep learning course that they can choose between period 1 (CSC_51054_EP) or period 2 (APM_52183_EP)
Strong mathematical background recommended

• Mandatory
  • Deep Learning (APM_52183_EP ): Kevin Scaman, INRIA
  • Reinforcement learning and autonomous agents (CSC_52081_EP): Jesse Read, École polytechnique
• Elective (1 course):
  • Optimization for AI (APM_52067_EP): Luiz Chamon and Aymeric Dieuleveut, École polytechnique
  • Optimization and responsible AI for sustainability (CSC_52073_EP): Sonia Vanier, École polytechnique
• Optional
  • Statistics in action (APM_52066_EP): Zacharie Naulet, INRAE
  • Advanced deep learning (CSC_52087_EP): Vicky Kalogeiton, Johannes Lutzeyer, Ye Zhu, and Xi Wang, École polytechnique
  • Introduction to text mining and NLP (CSC_52082_EP): Davide Buscaldi, Michalis Vazirgiannis
  • Multimodal generative AI (CSC_52002_EP): Vicky Kalogeiton
  • Graph machine and deep learning for generative AI (CSC_52072_EP): Johannes Lutzeyer and Michalis Vazirgiannis, École polytechnique
• Mandatory non-scientific courses:
  • Entrepreneurship for sustainability (IME_52068_EP, Chloé Steux) OR Case studies on innovation (IME_52062_EP, Philippe Ginier-Gillet)
  • Sport, Humanities, Foreign Language (these courses are similar to those every graduate from École polytechnique must follow)

Students must take one deep learning course that they can choose between period 1 (CSC_51054_EP) or period 2 (APM_52183_EP).

Research-oriented internship (4 to 6 months) (INT_52406_EP, 20 ECTS)

No refresher courses are provided in the M2. All M2 courses are 24h and will credit 2 ECTS.

• Deep reinforcement learning and multi-agent systems (CSC_53439_EP): Jesse Read, École polytechnique
• Large language models (CSC_53432_EP): Guokan Shang, MBZUAI
• Privacy and uncertainty quantification (APM_XXXX_EP): Paul Mangold, École polytechnique
• Uncertainty quantification and Bayesian inference (CSC_XXXX_EP)
• Constrained (reinforcement) learning (APM_XXXX_EP): Luiz F. O. Chamon, École polytechnique
• Explainable AI (APM_XXXX_EP)

• Introduction to the verification of neural networks (CSC_54441_EP): Eric Goubault and Sylvie Putot, École polytechnique
• Operational research intersects ML for explainability, sustainability, and frugality (CSC_XXXX_EP): Sonia Vanier, École polytechnique
• Fundamentals of security and robustness for AI (APM_XXXX_EP): El Mahdi El Mhamdi, École polytechnique
• Bias and fairness (APM_XXXX_EP): Solenne Gaucher, École polytechnique
• Explainability, security, privacy of LLMs (CSC_XXXX_EP): Davide Buscaldi, Université Sorbonne Paris Nord
• Fighting disinformation and detecting fake news (CSC_XXXX_EP): Ioana Manolescu, INRIA

(these courses span periods 1 and 2)

• Transverse project (MDC_54430_EP, 8 ECTS): Students will work half a day per week on a project corresponding to a challenging question raised either by an industrial partner or by a researcher in the domain spanned by the program.
• Seminar on ethical issues, law and novel applications of AI (IME_50430_EP, 6 ECTS): Véronique Steyer, École polytechnique
• Mandatory non-scientific courses: Sport, Humanities, Foreign Language (these courses are similar to those every graduate from École polytechnique must follow)

• Internship in an industrial or academic research lab (5 to 6 months) (INT_54490_EP, 24 ECTS)