Rémi Flamary

Professional website

MAP588 Emerging topics in Machine Learning


This course is done with Marylou Gabrié and is divided in two parts. I provide below the support for the first part of the course focusing on Optimal Transport for Machine Learning.

Course overview:

  • Introduction to Optimal Transport [PDF]
    • Optimal transport problem
    • Wasserstein distance and geometry
    • Computational aspects and regularized OT
  • Extensions of optimal transport.
  • Learning with optimal transport [PDF]
    • Mapping with Optimal Transport
    • Learning from histograms with Wasserstein distance
    • Learning from empirical distributions with Wasserstein distance
    • Learning from structured objects and across spaces

Practical session

We provide a short practical session introduction to discrete Optimal Transport. The material for te session (notebook + data) can be downloaded here.

Practical sessions will require a working Python environnement with the libraries Numpy/Scipy and Matplotlib installed. You can get such an environnement for Windows/Linux/MacOSX on Anaconda. Installing the POT Python Optimal Transport library will be necessary.

Here is a list of nice references and Python tutorials :

Additional references