Data Science

Mathematical Foundations of Machine Learning

Understanding of fundamental notions in Machine Learning (inference, ERM and SRM principles, generalization bounds, classical learning models, unsupervised learning, semi-supervised learning.

Modeling Seminar

This lecture proposes modelling problems. The problems can be industrial or academic.

Natural Language Processing & Information Retrieval

The automatic processing of languages, whether written or spoken, ...

Optimal transport: theory, applications and related numerical methods.

The goal of this course is to present a wide range of recent numerical methods and algorithms that find applications in various fields. More precisely, the course will focus on optimal transport algorithms, proximal methods and level set methods -- the leading application of these being image analysis.

Software Development Tools and Methods.

This lecture presents various useful applications, libraries and methods for software engineering related to applied mathematics.

Statistical learning: from parametric to nonparametric models

This course is related to mathematical and statistical methods which are very used in supervised learning.

Temporal, spatial and extreme event analysis

Modelling extreme temperatures, extreme river flows, earthquakes intensities, neuronal activity, map diseases, lightning strikes, forest fires, for example is a risk modelling and assessment task, which is tackled in statistics using point processes and extreme value theory.