Описание:The course addresses Bayesian methods for solving various machine learning and data processing problems (classification, dimension reduction, topic modeling, collaborative filtering, etc). Bayesian approach to probability theory allows one to take into account user's preferences and task specific properties when building the model. Besides, it offers an efficient framework for model selection. We will cover the problems of automatic feature selection, determination the number of components in probability mixtures, estimation the dimension of latent subspace, setting the regularization coefficients in an efficient way, etc. We will review several simple models that can be used as building blocks for the construction of more complex probabilistic models. General tools for building the probabilistic models and for designing inference algorithms in those models are presented in the course. We will end up with the basics of the probabilistic graphical models which are further extension of Bayesian framework. The course is developed with support of the joint MSU-Skoltech project (Joint Laboratory Agreement - No 081-R dated October 1, 2013, Appendix A2)
http://www.skoltech.ru/en/education/fall-2014-semester-term-2/
