Bayesian machine learning models

  1. Probabilistic formulation of machine learning problems. Bayesian interpretation of probabilities.
  1. Model selection problems. Bayesian model selection.
  1. Generalized linear models. Relevance vector machines. Automatic relevance determination.
  1. Local variational mathods. Bayesian regularization of logistic regression.
  1. General form of EM-algorithm.
  1. Probabilistic PCA.
  1. Variational Bayesian inference.
  1. Gaussian mixture model with automatic determination of the number of components.
  1. Monte Carlo Markov chain. Phase transitions in complex probabilistic models.
  1. Latent Dirichlet allocation as discrete mixture model.
  1. Gaussian processes for regression and classification.
  1. Dirichlet processes. Non-parametric Bayes.
  1. Expectation propagation and its application to recommender systems.
  1. Message-passing interface as general framework for disributed Bayesian inference.