Two refresher courses:
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:
3 courses among:
+ Mandatory non-scientific courses:
Mandatory:
1 course among:
+ Mandatory non-scientific courses:
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 language and graph modeling are welcome to follow the M1 refresher course in Computer Science. The detailed curriculum for M2 is currently under development. However, below we provide a preliminary overview of the core and introductory courses that students are expected to take.
CSC_53432_EP - Large Language Models (24h, 2 ECTS), Guokan Shang (MBZUAI) (contact: guokan.shang@mbzuai.ac.ae)
This course offers a deep dive into Large Language Models (LLMs), blending essential theory with hands-on labs to develop both practical skills and conceptual understanding—preparing you for roles in LLM development and deployment.
The curriculum begins with a brief overview of key historical NLP techniques. It then transitions to the transformer architecture, focusing on its attention mechanism and tokenization—the core of modern LLMs. Pre-training objectives such as masked/denoising language modeling and causal language modeling will also be covered, forming the basis for models like BERT, GPT, and T5. The course then examines LLM post-training techniques used to refine pre-trained models, including instruction tuning (SFT), reinforcement learning from human feedback (e.g., PPO/DPO), and reinforcement learning from verifiable rewards (e.g., GRPO). Finally, the course will address LLM application and future directions—including RAG, agents, multimodality, and alternative model architectures.
CSC_53431_EP - Analysis and Deep Learning on Geometric Data (24h, 2 ECTS), Maks Ovsjanikov (EP) (contact: maks@lix.polytechnique.fr)
This course will introduce students to advanced topics in modern geometric data analysis with focus on a) mathematical foundations (discrete differential geometry, mapping, optimization), and b) deep learning for best performing methods. We will give an overview of the foundations in shape analysis and processing before moving to modern techniques based on deep learning for solving problems such as shape classification, correspondence, parametrization, etc.
To be completed soon.
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).