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Accueil > Formations > Master MVA > Présentation des cours

Computational optimal transport

Lecturer : Gabriel PEYRE, (CNRS et Ecole Normale Supérieure Ulm)

Cours en anglais sauf si tous les étudiants sont francophones.

Objective of the course :

Optimal transport (OT) is a fundamental mathematical theory at the interface between optimization, partial differential equations and probability. It has recently emerged as an important tool to tackle a surprisingly large range of problems in data sciences, such as shape registration in medical imaging, structured prediction problems in supervised learning and training deep generative  networks.

This course will interleave the description of the mathematical theory with the recent developments of scalable numerical solvers. This will highlight the importance of recent advances in regularized approaches for OT which allow one to tackle high dimensional learning problems.

The course will feature numerical sessions using Python.

Organization of courses :

  • 6 courses of 2h30 + exam

Ressources :

- Gabriel Peyré and Marco Cuturi, Computational Optimal Transport, (course notes,slides, codes).

-Gabriel Peyré, The Numerical Tours of Data Science, (for the homework and the projects).