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--- curriculum [2025/07/25 08:06] – respai-vic
+++ curriculum [2025/11/06 13:26] (current) – [Period 2] respai-vic
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 course among:
-  * Deep Learning (Recommeded option, CSC_51054_EP, Michalis Vazirgiannis, EP)
+  * Deep Learning (Recommended option, CSC_51054_EP, Michalis Vazirgiannis, EP)
-  * Digital Representation and Analysis of Shapes (Recommeded option, CSC_51074_EP, Mathieu Desbru and Pooran Memari, EP & Inria)
+  * Digital Representation and Analysis of Shapes (Recommended option, CSC_51074_EP, Mathieu Desbru and Pooran Memari, EP & Inria)
-  * Signal Processing (Recommeded option, APM_51055_EP, Rémi Flamary, EP)
+  * Signal Processing (Recommended option, APM_51055_EP, Rémi Flamary, EP)
   * Topological Data Analysis (CSC_51056_EP, Steve Oudot, EP & Inria)
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 **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.
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 **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 techniques for content creation in 3D Computer Graphics, from 3D shapes to animated landscapes and character motion, with a focus on their link with AI. We first introduce user-centered Creative AI (also called Expressive modeling methods), i.e. smart 3D models - either based on knowledge or trained from examples, designed to help users  create/generate the 3D shapes their have in mind, and their organisation into complex, static or animated scenes. Second, we study the use of AI in Character Animation, from early motion planning and control methods to deep reinforcement learning solutions for generating the gaits of physically-based characters. These methods result into 3D agents 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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 > 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.
-**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)
+**CSC_54457_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.
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 > 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.
-**CSC_54443_EP - Soft robots: Design, Modeling, Simulation and Control (24h, 2 ECTS),  Christian Duriez (Inria Lille)** (contact: christian.duriez@inria.fr)
+**CSC_54444_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.