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curriculum [2025/07/16 12:35] – [Period 1] respai-viccurriculum [2025/07/31 16:35] (current) – [Period 1] respai-vic
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 ===== MASTER 1  (M1) ===== ===== MASTER 1  (M1) =====
  
 +Mandatory:
 +  * Refresher in Statistics (APM_51438_EP, Marine Le Morvan, Inria)
  
-  * MAP538: Refresher in Statistics (Marine Le Morvan) +1 course among: 
-  * INF538: Refresher in Computer Science (Marie-Paule Cani), presenting an introduction to 3D Computer Graphics +  * Refresher in Computer Graphics (CSC_51438_EP, Marie-Paule Cani, EP), presenting an introduction to 3D Computer Graphics  
 +  * Refresher in Computer Science (CSC_51440_EP, Amal Dev Parakkat, Telecom Paris)
  
-All subsequent M1 courses are 36h and will credit 4.5 ECTS. + 
 +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).
  
 ==== Period 1 ==== ==== Period 1 ====
  
 Mandatory: Mandatory:
-  * Computer Animation (INF585, Mathieu Desbrun and Marie-Paule Cani, EP)+  * Computer Animation (CSC_51085_EP, Mathieu Desbrun and Marie-Paule Cani, EP) 
 +  * Image Analysis and Computer Vision (CSC_51073_EP, Mathieu Brédif, EP & IGN) 
 +  * Machine Learning (MDC_51006_EP, Erwan Le Pennec and Jesse Reed, EP) 
  
 1 course among: 1 course among:
-  * Machine and Deep Learning (INF554, Michalis Vazirgiannis, EP)  +  * Deep Learning (Recommeded option, CSC_51054_EP, Michalis Vazirgiannis, EP)  
-  * Foundations of Machine Learning (MAP553, Erwan Le Pennec, EP)+  * Digital Representation and Analysis of Shapes (Recommeded option, CSC_51074_EP, Mathieu Desbru and Pooran Memari, EP & Inria) 
 +  * Signal Processing (Recommeded option, APM_51055_EP, Rémi Flamary, EP) 
 +  * Topological Data Analysis (CSC_51056_EP, Steve Oudot, EP & Inria)
  
-2 courses among:  ++ Mandatory non-scientific courses: 
-  * Topological Data Analysis (INF556, Steve Oudot, EP & Inria) +  * Introduction to Marketing and Strategy (IME_51456_EP, Philippe Ginier-Gillet, EP)
-  * Image Analysis and Computer Vision (INF573, Mathieu Brédif, EP & IGN) +
-  * Digital Representation and Analysis of Shapes (INF574, Mathieu Desbrun, Pooran Memari, EP & Inria) +
-  * Signal Processing (MAP555, Rémi Flamary, EP) +
- +
-+ Mandatory non-scientific courses +
- +
-  * Fundamental of Strategy and Innovation (MIE555) or Introduction to Marketing and Strategy (MIE556, Workload ++)+
   *  Sport   *  Sport
   *  Humanities   *  Humanities
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 ==== Period 2 ==== ==== Period 2 ====
  
-2 Mandatory courses: +Mandatory: 
-  * Advanced Machine Learning and Autonomous Agents (INF581, Jesse Read, EP) +  * Reinforcement Learning and Autonomous Agents  (CSC_52081_EP, Jesse Read, EP) 
-  * Computer Vision: From Fundamentals to Applications (INF5CV, Vicky Kalogeiton, EP)+  * Multimodal Generative AI (CSC_52002_EP, Vicky Kalogeiton, EP)
  
-2 scientific courses among  +2 courses among: 
-  * Advanced Deep Learning (INF581A, Michalis Vazirgiannis, Vicky Kalogeiton, Johannes Lutzeyer, EP)  +  * Deep Learning (Recommended option, APM_52183_EP, Kevin Scaman) 
-  * Image Synthesis: Theory and Practice (INF584, Tamy Boubekeur, Telecom ParisTech) +  * Image Synthesis: Theory and Practice (Recommended option, CSC_52084_EP, Tamy Boubekeur, Telecom ParisTech) 
-  * Real-time AI in Video Games : Decisive & Collaborative Actions (INF584A, David Bilemdjian, Chaire Ubisoft)  +  * Statistics in Action (Recommended option, APM_52066_EP, Zacharie Naulet, EP & INRAE) 
-  * Statistics in Action (MAP566, Zacharie Naulet, EP & INRAE) +  * Advanced Deep Learning (CSC_52087_EP, Michalis Vazirgiannis, Vicky Kalogeiton, Johannes Lutzeyer, EP)  
-  * Computational Optimal Transport for ML and Generative Modeling (MAP588, Rémi Flamary, EP)+  * Graph Representation Learning (CSC_52072_EP, Johannes Lutzeyer and Michalis Vazigiannis, EP) 
 +  * Emerging Topics in Machine Learning (APM_52188_EP, Rémi Flamary, EP)
  
-+ Mandatory non-scientific courses ++ Mandatory non-scientific courses: 
-  *  Startup and Large Companies: Building a Bridge for Innovation (MIE566, 3 ECTS)+  *  Either Entrepreneurship for Sustainability (IME_52068_EP, Chloé Steux, 3 ECTS) Or Case studies on Innovation (IME_52062_EP, Philippe Ginier-Gillet, 3 ECTS)
   *  Sport    *  Sport 
   *  Humanities    *  Humanities 
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 ==== Period 3 ==== ==== Period 3 ====
  
-MAP/INF590 - Research-Oriented Internship (4 to 6 months, 20 ECTS)+INT_52406_EP - Research-Oriented Internship (4 to 6 months, 20 ECTS)
  
  
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 **APM_53440_EP - Advanced unsupervised learning (24h, 2 ECTS), Pierre Latouche(UCA)** (contact: pierre.latouche@uca.fr)  **APM_53440_EP - Advanced unsupervised learning (24h, 2 ECTS), Pierre Latouche(UCA)** (contact: pierre.latouche@uca.fr) 
-> ABSTRACT TO BE ADDED+ 
 +This course aims at presenting advanced models and methods from computational statistics and machine learning for unsupervised learning, through the context of directed graphical models. Both the frequentist and Bayesian frameworks will be covered. In particular, we will study clustering methods and we will focus on mixture models and on the expectation maximisation algorithm for inference. The standard model selection criterion will be derived and we will illustrate their use to estimate the model complexity. After having given the theory of directed graphical models, we will show on a series of examples how the classical models from machine learning can be characterized in such a context. We will show how all the standard loss functions in machine learning can be linked to specific directed graphical models, giving rise to new ways to think about problems and to solve them. We will particularly insist on the interest of relying on directed graphical models to evaluate the complexity of the inference task for various models. The variational framework will be presented along with the variational EM and variational Bayes EM algorithms. The inference task of the stochastic block model, with a two-to-one dependency, will be described. The second part of the course will be about the use of deep neural networks in directed graphical models to obtain the so-called deep graphical models. Variational autoencoders and GAN we will be presented in such a context along with the corresponding inference strategies. We will finally show on a given example how the main model for social network analysis can be rewritten and extented with deep graphical models. We will point out the modifications in terms of directed graphical models, inference, and applications. In particular, we will describe the use of graph neural networks. Lectures will be done through slides and proofs on the blackboard. In labs, we will implement and test all the approaches seen in the lectures.  
 + 
 + 
  
          
-**APM_53441_EP - Learning with tabular data (24h, 2 ECTS), Marine Le Morvan** (contact: marine.le-morvan@polytechnique.edu)  +**APM_53441_EP - From Boosting to Foundation Models: learning with Tabular Data (24h, 2 ECTS), Marine Le Morvan** (contact: marine.le-morvan@polytechnique.edu)  
-> ABSTRACT TO BE ADDED+> This course offers a modern overview of statistical learning with tabular data, from classical tree-based models to emerging deep and foundation models. We will review the foundations of gradient boosting methods and their scalable implementations, cover recent deep learning models tailored for tabular data, and introduce tabular foundation models. In particular, we will discuss the limitations of LLMs on structured data, introduce the concept of in-context learning, and provide an in-depth understanding of novel tabular foundation models, including their architecture and pretraining strategies. The course will also address key practical challenges in real-world datasets and applications, such as encoding heterogeneous feature types (categorical, numerical, temporal, textual), strategies for handling missing data, and methods for evaluating and calibrating predictive uncertainty.
  
  
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 > 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.
  
-**CSC_53433_EP - Creative & Generative models in Computer Graphics, from shapes to motion (24h, 2 ECTS),  Marie-Paule Cani (EP), Julien Pettré (Inria)** (contact: marie-paule.cani@polytechnique.edu)+**CSC_53433_EP - Creative & 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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 > 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. 
  
-**CSC_54441_EP - 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) - Elective course
 > 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. 
 +
 +**CSC_54445_EP - Geometric Algorithms for Point Patterns and 2D embedded Structures, Across Applications in Visual Computing (24h, 2 ECTS), Pooran Memari** (contact: pooran.memari@polytechnique.edu) - Elective course
 +> This course explores the key role of 2D embedded structures, namely discrete distributions or point patterns in practical applications, such as object placement and texture synthesis. Through interconnected themes in data analysis, we will revisit foundational tools and introduce techniques to analyze, synthesize, design and further edit such 2D configurations. The exercise sessions present scenarios where geometric intuition leads to efficient computational methods, mainly via example-based modeling and lightweight learning techniques.
  
 **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>) **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>)
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 ==== Period 3 ==== ==== Period 3 ====
  
-MAP/INF690 - Internship either in the R&D department of a company or in a research lab  (5 to 6 months, 24 ECTS). +INT_54490_EP - Internship either in the R&D department of a company or in a research lab  (5 to 6 months, 24 ECTS). 
  
  
curriculum.1752669313.txt.gz · Last modified: 2025/07/16 12:35 by respai-vic
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