Responsible machine learning
This course will examine the mutual relationship between law, computer science and public policy. With the wide spread of artificial intelligence, the link between these domains is becoming more and more ambiguous. On one hand, the growth in computing capacity
Graphical Models: Discrete Inference and Learning
Description of the course: Graphical models (or probabilistic graphical models) provide a powerful paradigm to jointly exploit probability theory and graph theory for solving complex real-world problems. They form an indispensable component in several research areas, such as statistics, machine
Apprentissage pour les séries temporelles
Dans de nombreux contextes applicatifs (santé, économie, publicité…), les données recueillies prennent la forme de séries temporelles. L’enjeu fondamental consiste alors à choisir une représentation adaptée, permettant de tenir compte au mieux de l’information temporelle. Ce cours vise à fournir
Deep learning for medical imaging
Medical imaging technologies provide unparalleled means to study structure and function of the human body in vivo. Interpretation of medical images is difficult due to the need to take into account three-dimensional, time-varying information from multiple types of medical images.
Bayesian machine learning
By the end of the course, the students should have a high-level view of the main approaches to making decisions under uncertainty. be able to detect when being Bayesian helps and why. be able to design and run a Bayesian
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
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)
