List of courses

Introductory courses

These short refresher courses are held over the first few days of the academic year.

• A review of probability theory foundations (Paul Gassiat)

• Introduction to R (Robin Ryder)

• Introduction to Python (David Gontier)

• Introduction to Bayesian inference (Christian P. Robert)

Compulsory courses

All students must pass the following 4 courses:

• Optimization for Machine Learning (Gabriel Peyré, Clément Royer, Irène Waldspurger)

• High-dimensional Statistics (Vincent Rivoirard)

• Advanced Learning (Francis Bach)

• Graphical Models (Fabrice Rossi)

Optional courses

Students must select at least 5 options from this list:

• Applied Bayesian Statistics (Robin Ryder)

• Bayesian Asymptotics (Judith Rousseau)

• Bayesian Non-Parametrics and Bayesian Machine Learning (Guillaume Kon Kam King)

• Computational methods and MCMC (Christian P. Robert)

• Kernel Methods (Michael Arbel and Julien Mairal)

• Journalisme et Données (Robin Ryder and Sylvain Lapoix; taught in French)

• Large Language Models (Alexandre Allauzen)

• Mathematics of Deep Learning (Kevin Scaman)

• Mixing Times of Markov Chains (Justin Salez)

• Non-convex Inverse Problems (Irène Waldspurger)

• Optimal Transport (Gabriel Peyré)

• Reinforcement Learning (Ana Busic)

• Topological Data Analysis (Vincent Divol)