Apprentissage Profond pour la Restauration et la Synthese d’Images
La minituriasation des capteurs et l’évolution des capacités computationnelles ont conduit à une omniprésence des images. Cependant, ces images ont besoin de post-traitements de plus en plus exigeants (filtrages, restaurations) afin de produire des résultats de bonne qualité. Au coeur
Deep Learning in Practice / Advanced Deep Learning
Despite impressive mediatized results, deep learning methods are still poorly understood, neural networks are often difficult to train, and the results are black-boxes missing explanations, which is problematic given the societal impact of machine learning today (used as assistance in
Fondements Théoriques du deep learning
L’objectif principal de ce cours est de présenter la formulation mathématique des réseaux de neurones profonds, dans le cadre de leur utilisation pour la classification et la régression. Nous décrirons les différents problèmes, ainsi que des résultats mathématiques représentatifs de
Sequential learning
In online learning, data are acquired and treated on the fly; feedbacks are received and algorithms uploaded on the fly. This field has received a lot of attention recently because of the possible applications coming from internet. They include choosing
Graphs in machine learning
The graphs come handy whenever we deal with relations between the objects. This course, focused on learning, will present methods involving two main sources of graphs in ML: 1) graphs coming from networks, e.g., social, biological, technology, etc. and 2)
Foundations of Distributed and Large Scale Computing Optimization
The objective of this course is to introduce the theoretical background which makes it possible to develop efficient algorithms to successfully address these problems by taking advantage of modern multicore or distributed computing architectures. This course will be mainly focused
Kernel methods for machine learning
An introductory course on kernel methods for machine learning. Many problems in real-world applications of machine learning can be formalized as classical statistical problems, e.g., pattern recognition, regression or dimension reduction, with the caveat that the data are often not
Grandes matrices aléatoires application à l’apprentissage / Large Random Matrices and Application to machine learning
Introduction à la théorie des grandes matrices aléatoires et en particulier à ses applications au traitement du signal et à l’apprentissage.
Algorithms for speech and natural language processing
Speech and natural language processing is a subfield of artificial intelligence used in an increasing number of applications. This course will provide an overview and details of techniques and tasks used in the automatic processing of text and speech, covering
Problèmes inverses et imagerie : approches statistiques et stochastiques
Proposer différentes techniques d’analyse stochastique et statistique utiles pour résoudre des problèmes inverses et d’imagerie multi-capteurs (type échographie ultrasonore ou imagerie sismique). Présentation : here