Optimal Transport and Machine Learning Course 2022
Courses and practical sessions for the Optimal Transport and Machine learning course.
Course
The slides from the course can be downloaded here:
- Part 1 Intro to numerical Optimal Transport (N. Courty)
- Part 2 Unbalanced OT and OT across spaces (L. Chapel)
- Part 3 Optimal Transport for Machine Learning (R. Flamary)
Practical Sessions
You can download the introductory slides to the practical session here.
Install Python and POT Toolbox
In order to do the practical sessions you need to have a working Python installation. The simplest way on any OS is to install the Anaconda distribution that can be freely downloaded from here.
When anaconda is installed the simplest way to install pot is to launch the anaconda terminal and execute:
conda install -c conda-forge pot
which will install the POT OT Toolbox automatically. Note that in Window you need to launch the anaconda terminal with admnistrator mode to install with conda.
Download the Notebooks for the session
You can download all the necessary files here: OTML_DS3_2018.zip
The zip file contains the following session:
- Introduction to OT with POT
- Domain adaptation on digits with OT
- Color Grading with OT
- Word Mover's Distance on text
You can choose to do the practical session using the notebooks included or the python script. We recommend Notebooks for beginners.
The solutions for the practical sessions can be obtained at the following URL:
https://remi.flamary.com/cours/otml/solution_[NUMBER].zip
Where [NUMBER] has to be replaced by the integer part of the value of the Wasserstein distance obtained in Practical Session 0 using the Manhattan/Cityblock ground metric (without normalization of the marginals).