Erikursus masinõppes: Juhendamata õpe 2016/17 sügis

Reading list

• Machine Learning: A Probabilistic Perspective. Kevin P. Murphy, 2012.
• The Expectation Maximization Algorithm: A short tutorial, Sean Borman, 2004.
• Speech and Language Processing, Chapter 8. Daniel Jurafsky and James H. Martin, 2014.
• Bayesian inference with tears, Kevin Knight, 2009.
• Gibbs sampling for the uninitiated, Resnik and Hardisty, 2010.
• Latent Dirichlet Allocation, Blei et al., 2003.
• Tutorial: Variational inference for machine learning
• High-Level Explanation of Variational Inference

Videos

• MLSS2013, Non-parametric Bayes, lecture 1 - Introduction, Chinese Restaurant Process
• MLSS2013, Non-parametric Bayes, lecture 2 - Dirichlet process, Pitman-Yor Process
• MLSS2013, Non-parametric Bayes, lecture 3 - Indian Buffet process, hierarchial NPB models