This repository contains all the Homeworks / Exercices / Projects of the courses I took during M2 MVA in the academic year 2018 - 2019.
MVA (Mathématiques / Vision / Apprentissage) is held by the Mathematics Department of ENS Paris-Saclay and it helps students to develop mathematical modeling approaches to understand, emulate and predict the mechanisms of perception.
This year, 42 courses are provided by many top researchers all over Europe. The full list can be found here.
- Topological data analysis for imaging and machine learning: by Frédéric Chazal (INRIA), Julien Tierny (CNRS)
- Introduction à l'imagerie numérique: by Julie Delon (Université Paris Descartes), Yann Gousseau (Telecom ParisTech)
- Object recognition and computer vision (RECVIS): by Ivan Lpatev, Jean Ponce, Cordelia Schmid, Josef Sivic (ENS Ulm)
- Sub-pixel image processing: by Lionel Moisan (Université Paris Descartes)
- Convex optimization and applications in machine learning: by Alexandre d'Aspremont, (CNRS & Ecole Polytechnique)
- Probabilistic graphical models (PGM): by Francis Bach (INRIA), Nicolas Chopin (ENSAE)
- Reinforcement learning: by Alessandro Lazaric (FAIR)
- Deep Learning: by Vincent Lepetit (Unniversité de Bordeaux)
- Nuages de points et modélisation 3D: by François Goulette, Jean-Emmanuel Deschaud (MINES ParisTech), Tamy Boubekeur (Telecom ParisTech)
- Deformable models and geodesic methods for image analysis: by Laurent COHEN, Gabriel PEYRE (CNRS)
- Statistical computing on manifolds and data assimilation (Medical Imaging): by Hervé Delingette, Xavier Pennec (INRIA Sophia-Antipolis)
- Géométrie et espaces de formes: by Alain Trouvé (ENS de Cachan), Joan Alexis Glaunès (Université Paris Descartes)
- Kernel methods for machine learning: by Julien Mairal(INRIA), Jean-Philippe Vert (Mines ParisTech)
- Approches géométriques en apprentissage statistique - l’exemple des données longitudinales: by Stanley Durrleman(INRIA)
- Apprentissage par Réseaux de Neurones Profonds: by Stéphane Mallat(ENS Ulm)
- Deep Learning in Practice: by Guillaume Charpiat (INRIA), Edouard Oyallon (INRIA)