Probabilistic graphical models
F. BACH, G. OBOZINSKI
LearningTheory

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.

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

More information…

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

Francis Bach

(INRIA - Ecole Normale Supérieure)

Guillaume Obozinski

(Ecole des Ponts)

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