The course aims to provide students with basic understanding of modern Bayesian inference methods. The course will emphasize and discuss methods which have application in robotics, natural language processing, data mining, web search.
The details of the summer 2013 version of the course is available at https://ufal.mff.cuni.cz/modern-bayesian-methods-machine-learning-summer-2013/.
Introduction to Bayesian Machine learning and Bayesian networks.
Belief propagation and loopy belief propagation in Bayesian Networks.
Variational Bayes and expectation propagation.
 C. M. Bishop: Pattern Recognition and Machine Learning, vol. 4, no. 4. Springer, 2006, p. 738.
 K. Murphy: Machine Learning: a Probabilistic Perspective, the MIT Press (2012).
 D. Barber: Bayesian Reasoning and Machine Learning, Cambridge University Press (2012), available freely on the web.
 D. MacKay: Information Theory, Inference, and Learning Algorithms, Cambridge University Press (2003), available freely on the web at http://www.inference.phy.cam.ac.uk/mackay/itila/. It is also include video lectures.