Bayesian machine learning

Ects : 4

Enseignant responsable :

Volume horaire : 24

Description du contenu de l'enseignement :

Bayesian Nonparametrics:

  • Introduction
  • The Dirichlet Process
  • Infinite Mixture models
  • Posterior Sampling
  • Models beyond the Dirichlet Process
  • Gaussian Processes
  • Selected applications

Bayesian Deep Learning

  • Why do we want parameter uncertainty
  • Priors for Bayesian neural networks
  • Posterior inference
  • Martingale Posteriors and generalised Bayesian Inference

Pré-requis obligatoires :

  • Bayesian statistics
  • Markov Chain Monte Carlo

Compétence à acquérir :

Essentials of Bayesian Nonparametrics, main concepts for Bayesian Deep Learning

Mode de contrôle des connaissances :

Final exam and homework

Bibliographie, lectures recommandées

  • Hjort NL, Holmes C, Müller P, Walker SG, editors. Bayesian nonparametrics. Cambridge University Press; 2010 Apr 12.
  • Ghosal S, Van der Vaart AW. Fundamentals of nonparametric Bayesian inference. Cambridge University Press; 2017 Jun 26.
  • Williams CK, Rasmussen CE. Gaussian processes for machine learning. Cambridge, MA: MIT press; 2006.
  • Many references at www.gatsby.ucl.ac.uk/~porbanz/npb-tutorial.html
  • Murphy KP. Probabilistic machine learning: Advanced topics. MIT press; 2023 Aug 15.
  • Fong E, Holmes C, Walker SG. Martingale posterior distributions. Journal of the Royal Statistical Society Series B: Statistical Methodology. 2023 Nov;85(5):1357-91.