The objective of this subject is to address machine-learning problems from a Bayesian perspective. Graphical models (GMs) will be introduced as probabilistic models in which dependence and independence relations between random variables are described in terms of a graph. Similarly, Bayesian networks are a particular case of GMs that are especially useful for modeling conditional independences. Exact inference algorithms will be addressed (such as variable elimination, sum-product and junction tree) and the way they can be applied efficiently. These will be studied in this course alongside with the relation between inference and learning. More general approximate inference methods, either deterministic (e.g. Variational inference or expectation propagation) or based on sampling and simulation (e.g. Monte Carlo methods based on Markov chains), will also be introduced in this course.
David Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press 2012.
William M. Bostad. Introduction to Bayesian Statistics. Wiley-Interscience, 2007.
Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
Koller, D. & Friedman, N. Probabilistic Graphical Models: Principles and Techniques MIT Press, 2009.
Richard E. Neapolitan. Learning Bayesian Networks. Pearson Prentice Hall, 2004.
David J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.Introduction to time series and forecasting , P.J. Brockwell, R. A. Davis, Springer Texts in Statistics (1996)
Additional lecturers, if exist(name, position, degree): Alejandro Sierra Urrecho, Ph.D., Associate Professor ; Daniel Hernández Lobato, Ph.D., Associate Professor