Deep Learning
With the increase of computational power and amounts of available data, but also with the development of novel training algorithms and new whole approaches, many breakthroughs occurred over the few last years in Deep Learning for object and spoken language
Advanced learning for text and graph data ALTEGRAD
The ALTEGRAD course ( 28 hours) aims at providing an overview of state-of-the-art ML and AI methods for text and graph data with a significant focus on applications. Each session will comprise two hours of lecture followed by two hours
Introduction to statistical learning
The course presents the mathematical foundations for supervised learning.
Optimal Transport for Machine Learning
Optimal Transport (OT) is a foundational mathematical theory that connects optimization, partial differential equations, and probability. It offers a powerful framework for comparing probability distributions and has recently become an important tool in machine learning, especially for designing and evaluating
Introduction to Probabilistic Graphical Models and Deep Generative Models
This course provides a unifying introduction to probabilistic modelling through the framework of graphical models, together with their associated learning and inference algorithms. It is one of the few historical courses at the core of the MVA program. Recent developments
Convex optimization and applications in machine learning
L’objectif de ce cours est d’apprendre à reconnaître, manipuler et résoudre une classe relativement large de problèmes convexes émergents dans des domaines comme, par exemple, l’apprentissage, la finance ou le traitement du signal.
Computational Statistics
This course will detail statistical computational methods and bayesian inference. The course will start with the stochastic gradient algorithm, its theoretical properties and numerical bottlenecks. After a brief introduction to Bayesian inference we will discuss the numerical challenges when computing
Méthodes mathématiques pour les neurosciences
Nous présentons dans ce cours quelques outils mathématiques qui interviennent de manière systématique dans de nombreux problèmes de modélisation en neurosciences.
Image denoising : the human machine competition
1-Explore the structure of images at « patch » level. (Patches are small image extracts that are processed in computational neural networks and in recent image processing. The current dimension that start being well understood is about 8×8=64 to 60×60=3600) 2-Apply it
3D computer vision
Explore the theoretical foundations of 3D computer vision from multiple views, with emphasis on binocular stereo, and show the practical limitations in the algorithmic state of the art. Présentation : here