Prè-requis
Course on Probability
Objectif du cours
Provide a unifying introduction to probabilistic modelling through the framework of graphical models, together with their associated learning and inference algorithms.
Presentation : here
Organisation des séances
- 9 classes of 3 hours each
- All classes and materials will be in English. All interactions, homeworks, exams may be done in French or English
Mode de validation
- 3 homeworks with simple implementations of algorithms (Matlab, R, Python ou autre)
- 1 final exam
- Reading a research paper and write a small report (less than 4 pages)
Références
Chris Bishop. Pattern Recognition and Machine Learning. Springer, 2006
Thèmes abordés
- Maximum likelihood
- Linear regression
- Logistic regression
- Mixture models and clustering
- Directed and undirected graphical models
- Exponential family
- Sum-product algorithm and exact inference
- Hidden Markov models
- Approximate inference
- Bayesian methods
Les intervenants