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--- curriculum [2025/10/28 13:51] – [Period 2] director-trai
+++ curriculum [2025/10/28 15:32] (current) – [Period 2] director-trai
@@ Line 3: / Line 3: @@
 ===== First year (M1) =====
-Mandatory:
+  * **Mandatory:** //Refresher in statistics// (APM_51438_EP): Marine Le Morvan, Inria
-  * Refresher in statistics (APM_51438_EP): Marine Le Morvan, Inria
+  * **Optional:** //Refresher in computer science// (CSC_51440_EP): Amal Dev Parakkat, Télécom Paris
-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. Note that students need to mandatorily either choose a Deep Learning course either in Period 1 (CSC_51054_EP) or in Period 2 (APM_52183_EP).
+All subsequent M1 courses are 36h and will credit 4.5 ECTS.
 ==== Period 1 ====
-Mandatory:
+  * **Mandatory:** //Machine Learning// (MDC_51006_EP): Erwan Le Pennec and Jesse Reed, École polytechnique
-  * 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
-At least 1 course among:
+    * //Emerging subjects in machine learning and collaborative learning// (APM_51178_EP, research-oriented): Aymeric Dieleveut and El Mahdi El Mhamdi, École polytechnique
-  * Deep Learning (CSC_51054_EP): Michalis Vazirgiannis and Johannes Lutzeyer, École polytechnique
+    * //Signal processing from Fourier to ML// (APM_51055_EP): Rémi Flamary, École polytechnique
-  * Emerging subjects in machine learning and collaborative learning (APM_51178_EP, research-oriented): Aymeric Dieleveut and El Mahdi El Mhamdi, École polytechnique
+  * **Optional:**
-  * Signal processing from Fourier to ML (APM_51055_EP): Rémi Flamary, École polytechnique
+    * :!: //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
-Optional:
+    * //Image analysis and computer vision// (CSC_51073_EP): Mathieu Brédif, Université Gustave Eiffel
-  * Statistical learning theory (APM_51059_EP :!:): Karim Lounici, École polytechnique
+    * :!: //Topological data analysis// (CSC_51056_EP): Steve Oudot, INRIA
-  * Probability theory for ML - Applications to Monte Carlo methods and generative models (APM_51056_EP :!:): Alain Durmus, École polytechnique
+  * Mandatory non-scientific courses:
-  * Image analysis and computer vision (CSC_51073_EP): Mathieu Brédif, Université Gustave Eiffel
+    * //Introduction to Marketing and Strategy// (IME_51456_EP): Philippe Ginier-Gillet, École polytechnique
-  * Topological data analysis (CSC_51056_EP :!:): Steve Oudot, INRIA
+    * //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
-  * Humanity
-  * Foreign Language
 ==== Period 2 ====
-Mandatory:
+  * **Mandatory**
-  * Reinforcement learning and autonomous agents (CSC_52081_EP): Jesse Read, École polytechnique
+    * //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
+  * **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)
-optimization course:
+:?: Students must take one deep learning course that they can choose between period 1 (CSC_51054_EP) or period 2 (APM_52183_EP).
-  * 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 ====
-INT_52406_EP - Research-Oriented Internship (4 to 6 months, 20 ECTS)
+Research-oriented internship (4 to 6 months) (INT_52406_EP, 20 ECTS)
 \\ \\ \\
@@ Line 72: / Line 59: @@
 ===== Second year (M2) =====
-No refresher course is provided but students directly entering in the M2 and lacking background in Computer Graphics are welcome to follow the M1 refresher course in Computer Science.
+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 Period 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)
-**MDC_54430_EP - Transverse project (8 ECTS):** Students will work half a day a week on a transverse project, corresponding to a challenging question either raised by an industrial partner or by a researcher in the domain spanned by the programme.
-**IME_50430_EP - Seminar on ethical issues, law and novel applications of AI (6 ECTS), Véronique Steyer <veronique.steyer@polytechnique.edu>**
-Students will be sensitized to ethical issues and law, and introduced to novel applications of artificial intelligence and visual computing through a weekly seminar with key-note talks from both institutional and industrial partners.
-**Courses in humanities, languages and sports (8 ECTS total)** These courses will be similar to those of the other graduate degrees at Ecole Polytechnique.
 ==== Period 3 ====
-INT_54490_EP - Internship either in the R&D department of a company or in a research lab  (5 to 6 months, 24 ECTS).
+  * Internship in an industrial or academic research lab (5 to 6 months) (INT_54490_EP, 24 ECTS)