Teaching

This is the homepage for my course in Master 2 Mathématiques, Vision, Apprentissage - MVA.

Description

Evolution of the course:

• Since 2019: Computational Optimal Transport
• 2017-2019: Mathematical Foundation of Data Sciences
• 2008-2016: Sparsity and compressed sensing

The course presents an overview of the mathematics of data sciences. This includes in particular tools from convex optimization, compressed sensing and optimal transport. It showcases application in machine learning and imaging sciences.

Pre-requisite

Basics of linear algebra, calculus and Fourier transform.

Validation of the course

Attending all the numerical tours, a mini-project with a report and an oral presentation. Note that there is a special session dedicated to the preparation of the projects.

Resources

• Numerical Tours: a repository to Matlab/Scilab/Python explorations of mathematical data processing.
• Mathematical Tours: course notes on data sciences and general audience articles.
• Computational Optimal Transport: site web for a book on computational OT.

Other MVA courses on the web

• Yann Gousseau, Julie Delon (#1)
• Yann Gousseau, Julie Delon (#2)
• Lionel Moisan
• Pascal Monasse, Renaud Marlet
• Jean-Michel Morel
• Iasonas Kokkinos
• Gabriel Peyré
• Emmanuel Bacry
• Laurent Cohen, Gabriel Peyré
• Andrés Almansa, Florence Tupin, Jean-Marie Nicolas
• Hervé Delingette, Xavier Pennec (#1)
• Hervé Delingette, Xavier Pennec (#2)
• Francis Bach, Guillaume Obozinski
• Olivier Faugeras, Jonathan Touboul, Romain Veltz
• Remi Munos
• Nicolas Vayatis
• Arnak Dalalyan
• Alexandre d’Aspremont
• Jean-Pierre Nadal
• Alain Trouvé, Joan Glaunes
• Jean-Philippe Vert