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| curriculum [2025/06/17 13:06] – [Transverse Courses and Projects (spanning Period 1 and 2)] respai-vic | curriculum [2025/06/23 11:42] (current) – respai-vic |
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| * MAP538: Refresher in Statistics (Marine Le Morvan) | * MAP538: Refresher in Statistics (Marine Le Morvan) |
| * INF538: Refresher in Computer Science (Amal Dev Parakkat) | * INF538: Refresher in Computer Science (Marie-Paule Cani), presenting an introduction to 3D Computer Graphics |
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| All subsequent M1 courses are 36h and will credit 4.5 ECTS. | All subsequent M1 courses are 36h and will credit 4.5 ECTS. |
| ==== Period 1 ==== | ==== Period 1 ==== |
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| **MAP/INF631 - Advanced unsupervised learning (24h, 2 ECTS), Pierre Latouche(UCA)** (contact: pierre.latouche@uca.fr) (A CONFIRMER, ABSTRACT TO BE ADDED) | **APM_53440_EP - Advanced unsupervised learning (24h, 2 ECTS), Pierre Latouche(UCA)** (contact: pierre.latouche@uca.fr) |
| | > ABSTRACT TO BE ADDED |
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| **MAP/INF XXXXX - Advanced unsupervised learning (24h, 2 ECTS), Tabular Learning (4 séances) - Marine Le Morvan** | **APM_53441_EP - Learning with tabular data (24h, 2 ECTS), Marine Le Morvan** (contact: marine.le-morvan@polytechnique.edu) |
| (contact: <marine-le-morvan@hotmail.fr) (ABSTRACT TO BE ADDED) | > ABSTRACT TO BE ADDED |
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| **INF632 - Introduction to NLP and LLMs (24h, 2 ECTS), Guokan Shang (EP) ** (contact: guokan.shang@polytechnique.edu) | **CSC_53432_EP - Large Language Models (24h, 2 ECTS), Guokan Shang (MBZUAI) ** (contact: guokan.shang@mbzuai.ac.ae) |
| > This course offers an extensive and in-depth exploration of natural language processing (NLP) and large language models (LLMs), blending foundational principles with advanced techniques. The curriculum spans the domain of NLP, starting from basic concepts like indexing, Bag-of-Words, and TF-IDF, and extending to more sophisticated methods such as Graph of Words, Word2Vec, fastText, and GloVe. Then the focus shifts to deep learning models in NLP, including 1D-CNN, RNN, Hierarchical Attention Networks (HAN), and Neural Machine Translation (NMT). Additionally, It delves into essential evaluation metrics and benchmark. Following that, the course presents the cutting-edge transformer architecture and pretraining techniques such as masked and causal language modelling. Key models including BERT, GPT, and BART are studied in detail, highlighting their transformative impact on the field and all their applications. The course also covers the emergence of LLMs introduced by ChatGPT, emphasizing prompting techniques, model adaptation using methods like LoRA, and model quantization techniques including techniques like QLoRA that merges both model adaptation and quantization. This comprehensive course integrates hands-on lab sessions using PyTorch and Keras, ensuring students gain practical experience alongside theoretical knowledge, preparing them for advanced roles in data science and NLP. | > 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. |
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| | **CSC_53439_EP - Deep Reinforcement Learning (24h, 2 ECTS), Jesse Read** (contact: jesse.read@polytechnique.edu) |
| **INF639 - Deep Reinforcement Learning (24h, 2 ECTS), Jesse Read** (contact: jesse.read@polytechnique.edu) | |
| > Reinforcement learning (RL) is of increasing relevance today, including in games, complex energy systems, recommendation engines, finance, logistics, and for auto-tuning the parameters of other learning frameworks. This course assumes familiarity with the foundations of RL and its main paradigms (temporal-difference learning, Monte Carlo, and policy-gradient methods). We will explore them further, and study modern state-of-the-art variants (such as proximal policy optimization), with a focus on developing RL solutions with deep neural architectures suited to modern applications. We will also take a look at specialized topics such inverse reinforcement learning. | > Reinforcement learning (RL) is of increasing relevance today, including in games, complex energy systems, recommendation engines, finance, logistics, and for auto-tuning the parameters of other learning frameworks. This course assumes familiarity with the foundations of RL and its main paradigms (temporal-difference learning, Monte Carlo, and policy-gradient methods). We will explore them further, and study modern state-of-the-art variants (such as proximal policy optimization), with a focus on developing RL solutions with deep neural architectures suited to modern applications. We will also take a look at specialized topics such inverse reinforcement learning. |
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| **INF631 - Analysis and Deep Learning on Geometric Data (24h, 2 ECTS), Maks Ovsjanikov (EP)** (contact: maks@lix.polytechnique.fr) | **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. | > 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. |
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| **INF633 - Creative and Generative models in Computer Graphics (24h, 2 ECTS), Marie-Paule Cani (EP), Julien Pettré (Inria)** (contact: marie-paule.cani@polytechnique.edu) | **CSC_53433_EP - Creative and Generative models in Computer Graphics (24h, 2 ECTS), Marie-Paule Cani (EP), Julien Pettré (Inria)** (contact: marie-paule.cani@polytechnique.edu) |
| > This course presents the AI-related methods developed in Computer Graphics to create or generate individual 3D shapes, animated landscapes and humanoid motion. We first introduce user-centered Creative AI, i.e. smart 3D models - either based on knowledge or trained from examples, designed to help users creating and controlling 3D shapes and environments. Second, we focus on the use of AI in Character Animation, from early motion planning and control methods to deep reinforcement learning solutions. These methods result into 3D character models able to navigate alone or within crowds, and to interact with their environment. The lab sessions are held on Unity, based on C#. | > This course presents the AI-related methods developed in Computer Graphics to create or generate individual 3D shapes, animated landscapes and humanoid motion. We first introduce user-centered Creative AI, i.e. smart 3D models - either based on knowledge or trained from examples, designed to help users creating and controlling 3D shapes and environments. Second, we focus on the use of AI in Character Animation, from early motion planning and control methods to deep reinforcement learning solutions. These methods result into 3D character models able to navigate alone or within crowds, and to interact with their environment. The lab sessions are held on Unity, based on C#. |
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| ==== Period 2 ==== | ==== Period 2 ==== |
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| **INF657G - Navigation for Autonomous systems (24h, 2 ECTS), David Filliat (ENSTA)** (contact: david.filliat@ensta-paris.fr) | **CSC_54456_EP - Navigation for Autonomous systems (24h, 2 ECTS), David Filliat (ENSTA)** (contact: david.filliat@ensta-paris.fr) |
| > Drones and robots must create maps of their surroundings to plan their movement and navigate. This course presents the robotic platforms and the most common sensors (vision, Lidar, intertial units, odometry …) and the different components of navigation: control; obstacle avoidance; localization; mapping (SLAM) and trajectory planning as well as filtering (Kalman filter, particle filtering, etc.) and optimization techniques used in these fields. | > Drones and robots must create maps of their surroundings to plan their movement and navigate. This course presents the robotic platforms and the most common sensors (vision, Lidar, intertial units, odometry …) and the different components of navigation: control; obstacle avoidance; localization; mapping (SLAM) and trajectory planning as well as filtering (Kalman filter, particle filtering, etc.) and optimization techniques used in these fields. |
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| **INF641 - Introduction to the verification of neural networks (24h, 2 ECTS), Eric Goubault (EP), Sylvie Putot (EP)** (contact: sylvie.putot@polytechnique.edu) | **CSC_54441_EP - Introduction to the verification of neural networks (24h, 2 ECTS), Eric Goubault (EP), Sylvie Putot (EP)** (contact: sylvie.putot@polytechnique.edu) |
| > Neural networks are widely used in numerous applications including safety-critical ones such as control and planning for autonomous systems. A central question is how to verify that they are correct with respect to some specification. Beyond correctness or robustness, we are also interested in questions such as explainability and fairness, that can in turn be specified as formal verification problems. In this course, we will see how formal methods approaches introduced in the context of program verification can be leveraged to address the verification of neural networks. | > Neural networks are widely used in numerous applications including safety-critical ones such as control and planning for autonomous systems. A central question is how to verify that they are correct with respect to some specification. Beyond correctness or robustness, we are also interested in questions such as explainability and fairness, that can in turn be specified as formal verification problems. In this course, we will see how formal methods approaches introduced in the context of program verification can be leveraged to address the verification of neural networks. |
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| **INF642 - Socio-emotional embodied conversational agents (24h, 2 ECTS), Chloé Clavel (Inria Paris) and Brian Ravenet (Université Paris Saclay)** (contacts: chloe.clavel@inria.fr and brian.ravenet@lisn.upsaclay.fr>) | **CSC_54442_EP - Socio-emotional embodied conversational agents (24h, 2 ECTS), Chloé Clavel (Inria Paris) and Brian Ravenet (Université Paris Saclay)** (contacts: chloe.clavel@inria.fr and brian.ravenet@lisn.upsaclay.fr>) |
| > Many interactive systems, from virtual companions to online retailing, rely on embodied conversational agents. These agents need to reach a good level of communication skills to conduct a conversation with humans and be acceptable and trustworthy by humans. This course will introduce non-verbal behavior models, present models for multimodal dialog, opinion detection and voice quality, explain how to model the agent's emotions and their evolution over time, and present methods for enhancing naturalism with expressive gaze and gestures, realistic animation. | > Many interactive systems, from virtual companions to online retailing, rely on embodied conversational agents. These agents need to reach a good level of communication skills to conduct a conversation with humans and be acceptable and trustworthy by humans. This course will introduce non-verbal behavior models, present models for multimodal dialog, opinion detection and voice quality, explain how to model the agent's emotions and their evolution over time, and present methods for enhancing naturalism with expressive gaze and gestures, realistic animation. |
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| **INF644 - Virtual/Augmented Reality & 3D Interactions (24h, 2 ECTS), Anatole Lécuyer (Inria Rennes), Ferran Argelaguet (Inria Rennes), Arnaud Prouzeau (Inria Saclay), Claudio Pacchierotti (CNRS - IRISA), Fabien Lotte (Inria Bordeaux)** (contact: anatole.lecuyer@inria.fr) | **CSC_54444_EP - Virtual/Augmented Reality & 3D Interactions (24h, 2 ECTS), Anatole Lécuyer (Inria Rennes), Ferran Argelaguet (Inria Rennes), Arnaud Prouzeau (Inria Saclay), Claudio Pacchierotti (CNRS - IRISA), Fabien Lotte (Inria Bordeaux)** (contact: anatole.lecuyer@inria.fr) |
| > Metaverse and virtual/augmented reality technologies are spreading widely. But reconstructing our world and generating virtual ones would be useless without effective techniques to navigate and interact with them. This course will present virtual and augmented reality systems as well as the associated methods for 3D interaction, from multi-modal interaction merging visual immersion, sound and haptics systems to brain-computer interfaces. | > Metaverse and virtual/augmented reality technologies are spreading widely. But reconstructing our world and generating virtual ones would be useless without effective techniques to navigate and interact with them. This course will present virtual and augmented reality systems as well as the associated methods for 3D interaction, from multi-modal interaction merging visual immersion, sound and haptics systems to brain-computer interfaces. |
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| **INF634 - 3D Computer Vision (24h, 2 ECTS), Xi Wang (EP) TO BE UPDATED** (contact: Xi.Wang@polytechnique.edu) | **CSC_54434_EP - 3D Computer Vision (24h, 2 ECTS), Xi Wang (EP)** (contact: Xi.Wang@polytechnique.edu) |
| > (ABSTRACT TO BE ADDED) | > This course presents modern 3D computer vision in a clear, step-by-step progression: we begin with classical multi-view reconstruction and structure‑from‑motion pipelines, then advance to neural implicit representations for novel‑view synthesis (e.g., NeRF), proceed to explicit geometry rendering via 3D Gaussian Splatting (3DGS), and finally explore generative 3D models: e.g., 3D generation guided by 2D generative score distillation (DreamFusion-like), data‑driven 3D/4D content generation for dynamic scenes and motion, and the latest video generation techniques. |
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| **INF643 - Soft robots: Design, Modeling, Simulation and Control (24h, 2 ECTS), Christian Duriez (Inria Lille)** (contact: christian.duriez@inria.fr) | **CSC_54443_EP - Soft robots: Design, Modeling, Simulation and Control (24h, 2 ECTS), Christian Duriez (Inria Lille)** (contact: christian.duriez@inria.fr) |
| > Soft robotics is a promising novel field, bringing more robustness in robots design and for all tasks involving close interactions with humans, from help to disable people to medical robot. This course will give an introduction to recent advances in soft robotics, including design, modeling, simulation and control techniques for robots, and will present recent applications in medicine, industry and art. | > Soft robotics is a promising novel field, bringing more robustness in robots design and for all tasks involving close interactions with humans, from help to disable people to medical robot. This course will give an introduction to recent advances in soft robotics, including design, modeling, simulation and control techniques for robots, and will present recent applications in medicine, industry and art. |
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| A new course is to be added | |
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| ==== Transverse Courses and Projects (spanning Period 1 and 2) ==== | ==== Transverse Courses and Projects (spanning Period 1 and 2) ==== |
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| **MAP/INF630 - 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 graduate degree. | **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. |
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| **MIE630 - Seminar on ethical issues, law and novel applications of AI (6 ECTS), Véronique Steyer <veronique.steyer@polytechnique.edu>, Louis Vuarin <louis.vuarin@polytechnique.edu>:** | **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. | 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. |
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