Description

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

News

Exam dates and room: on Friday the 12th of January, 14h - 17h15.

• on paper
• no documents or machines allowed
• annal from last year download here (careful: I was not in charge of the course)

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.
• 3ème séance : Introduction to HMMs + Bonus practical session on discrete Markov chains
• 4ème séance : Inference in HMMs : Forward-Backward & EM (aka Baum-Welch)
• 5ème séance : Viterbi algorithm and introduction to SBM + Practical session on HMMs: EM and Viterbi
• 6ème séance : Variational inference & illustration with SBM

Corrections

On the fly version of the code during TD on GMM downloadable here

Ressources en ligne