===== Detailed Curriculum =====

===== First year (M1) =====

  * **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.

==== Period 1 ====

  * **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



==== Period 2 ====

  * **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).

==== Period 3 ====

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

\\ \\ \\


===== Second year (M2) =====

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

==== Period 1 ====

  * //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)
==== Period 2 ====

  * //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

==== Transverse courses and projects ====
**(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)


==== Period 3 ====

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