The course aims to provide students with basic understanding of modern Bayesian inference methods. The course
will be composed of a series of lectures presented by experts from the Machine Learning Group, Cambridge
University, UK. More information is available at
https://sites.google.com/site/filipjurcicek/teaching/bayesian-inference.
Last update: Helena Kisvetrová (15.02.2013)
Předmět je zaměřen na seznámení studentů s moderními metodami Bayesovské inference. Forma předmětu je
formou přednášek pozvaných odborníků z Machine Learning Group, Cambridge Univerzity, UK. Více informací je
dostupných na https://sites.google.com/site/filipjurcicek/teaching/bayesian-inference.
Literature -
Last update: JUDr. Dana Macharová (14.02.2013)
[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.
Last update: JUDr. Dana Macharová (13.02.2013)
[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.
Syllabus -
Last update: JUDr. Dana Macharová (14.02.2013)
Lecture topics:
Introduction to Bayesian Machine Learning: probabilistic models, inference, Bayes rule.
Approximate Inference: sampling methods, variational Bayes and expectation propagation.
Non-parametric Bayesian Methods: Gaussian processes and Dirichlet processes.
Bayesian Sparsity: spike and slab priors, dependency in sparsity enforcing priors, group sparsity.