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Accueil > Formations > Master MVA > Présentation des cours
Probabilistic graphical models
Lecturer : Pierre LATOUCHE (U. de Paris) - Pierre-Alexandre MATTEI (INRIA)
Objective of the course :
Provide a unifying introduction to probabilistic modelling through the framework of graphical models, together with their associated learning and inference algorithms.
Topics :
- 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
Prerequisites :
Course on Probability
Organization of courses :
- 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
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)
- Si rattrapage: oral
References :
Chris Bishop. Pattern Recognition and Machine Learning. Springer, 2006