Description

Chargé de cours : Nicolas Jouvin
Horaires : Mercredi 8h30-11h45.

News

Two internship proposal on physics-informed neural network :

1. Generative PINNs
2. PINNs for mechanistic-statistical models

Up-to-date version of the slides: here (there will be frequent updates, keep up to date !)

Outline

1. Introduction to Bayesian statistics
2. Clustering with finite mixture models
3. The EM algorithm
4. Hidden Markov Models
5. Stochastic Block Model and introduction to variational inference

Séances

• 1ère séance : 3h class + Dowload TD1 exercice sheet + Bonus practical session
• 2eme séance : Expectation-Maximization algorithm. Download the TD2 exercise sheet + live correction.
• 3ème séance : Introduction to HMMs + Bonus practical session on discrete Markov chains

Corrections

Ressources en ligne

• Stéphane Robin’s Lecture
• Sophie Donnet’s Lecture
• Chistopher M. Bishop’s Book : for this course, relevant chapters are mostly 8, 9 & 10 (chapter 12 on continuous latent variables can be useful as well, the core ideas do not change)
• Kevin P. Murphy’s Book