Ects : 4

Enseignant responsable :

• GUILLAUME KON KAM KING

Volume horaire : 18

Description du contenu de l'enseignement :

Bayesian nonparametrics:

• Basics: infinite mixture models and clustering
• Models beyond the Dirichlet process
• Posterior sampling
• Applications

Gaussian Processes

Bayesian Deep Learning

Pré-requis obligatoires :

Bayesian statistics

Compétence à acquérir :

Essential concepts of Bayesian nonparametrics

Essentials of Bayesian Deep Learning

Bibliographie, lectures recommandées

• Hjort, N. L., Holmes, C., Müller, P., & Walker, S. G. (Eds.). (2010). Bayesian nonparametrics (Vol. 28). Cambridge University Press.
• Orbanz, P., & Teh, Y. W. (2010). Bayesian nonparametric models. Encyclopedia of machine learning, 1, 81-89.
• Müller, P., Quintana, F. A., Jara, A., & Hanson, T. (2015). Bayesian nonparametric data analysis (Vol. 1). New York: Springer.
• Ghosal, S., & van der Vaart, A. W. (2017). Fundamentals of nonparametric Bayesian inference (Vol. 44). Cambridge University Press.
• Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.
• Murphy, K. P. (2023). Probabilistic machine learning: Advanced topics. MIT press.