MScT TRAI

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. 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).

Mandatory:

• Machine Learning (MDC_51006_EP): Erwan Le Pennec and Jesse Reed, École polytechnique

At least 1 course among:

• 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

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

Mandatory:

• Reinforcement learning and autonomous agents (CSC_52081_EP): Jesse Read, École polytechnique
• Deep Learning (APM_52183_EP): Kevin Scaman, INRIA

1 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

1 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

INT_52406_EP - Research-Oriented Internship (4 to 6 months, 20 ECTS)

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.

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.

INT_54490_EP - Internship either in the R&D department of a company or in a research lab (5 to 6 months, 24 ECTS).