Professor Daphne Koller of Stanford University (Rajeev Motwani Professor in the School of Engineering), will be instructing a free online course in Probabilistic Graphical Models.
In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques; you will also learn algorithms for using a PGM to reach conclusions about the world from limited and noisy evidence, and for making good decisions under uncertainty. The class covers both the theoretical underpinnings of the PGM framework and practical skills needed to apply these techniques to new problems.
(i) The Bayesian network and Markov network representation, including extensions for reasoning over domains that change over time and over domains with a variable number of entities;
(ii) reasoning and inference methods, including exact inference (variable elimination, clique trees) and approximate inference (belief propagation message passing, Markov chain Monte Carlo methods);
(iii) learning methods for both parameters and structure in a PGM;
(iv) using a PGM for decision making under uncertainty. The course will also draw from numerous case studies and applications, so that you'll also learn how to apply PGM methods to computer vision, text understanding, medical decision making, speech recognition, and many other areas.
You can register for the course here.