SIS code: 
summer s.:5
2/1 C(+Ex)

Bayesian inference

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 course will be presented in English and it will be based on the the machine learning course 4F13 taught by Carl Edward Rasmussen and Zoubin Ghahramani at Cambridge University Engineering Department. The following link includes slides from this course as well as the practicals that the students are expected to do:


The details of the summer 2013 version of the course is available at

Video recordings at YouTube:

Slides recordings at YouTube:


Lecture topics:

  • Introduction to Bayesian Machine learning and Bayesian networks.
  • Belief propagation and loopy belief propagation in Bayesian Networks.
  • Variational Bayes and expectation propagation.
  • Sampling methods.


The material covered in the lectures can be found in recent textbooks:

[1] D. Koller, N. Friedman: Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series), The MIT Press, 2009, p. 1280

[2] C. M. Bishop: Pattern Recognition and Machine Learning, vol. 4, no. 4. Springer, 2006, p. 738.

[3] K. Murphy: Machine Learning: a Probabilistic Perspective, the MIT Press (2012). 

[4] D. Barber: Bayesian Reasoning and Machine Learning, Cambridge University Press (2012), available freely on the web.

[5] D. MacKay: Information Theory, Inference, and Learning Algorithms, Cambridge University Press (2003), available freely on the web at It is also include video lectures.

[6] C. Rasmussen, Z. Ghahramani:  4F13 Machine Learning taught at Cambridge University Engineering Department. Slides and the practicals: