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--- curriculum [2025/10/28 15:13] – [Period 1] director-trai
+++ curriculum [2025/10/28 15:32] (current) – [Period 2] director-trai
@@ Line 21: / Line 21: @@
     * //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 non-scientific courses:
-  * Introduction to Marketing and Strategy (IME_51456_EP): Philippe Ginier-Gillet, École polytechnique
-  * Sport
-  * Humanities
-  * Foreign Language
 ==== Period 2 ====
-Mandatory:
+  * **Mandatory**
-  * Deep Learning (APM_52183_EP :?:): Kevin Scaman, INRIA
+    * //Deep Learning// (APM_52183_EP :?:): Kevin Scaman, INRIA
-  * Reinforcement learning and autonomous agents (CSC_52081_EP): Jesse Read, École polytechnique
+    * //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).
-optimization 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
-course among:
-  * 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
 ==== Period 3 ====
-  * Research-oriented internship (4 to 6 months) (INT_52406_EP, 20 ECTS)
+Research-oriented internship (4 to 6 months) (INT_52406_EP, 20 ECTS)
 \\ \\ \\
@@ Line 69: / Line 59: @@
 ===== 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.
-==== Transverse courses and projects (spanning periods 1 and 2) ====
+  * //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)
-  * 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