CSE 703: Machine Learning of Probabilistic Graphical Models
This page refers to the Fall 2012 offering of CSE 703 only. The information on this page does not necessarily apply to every offering of CSE 703.
Fall 2012
12656
Study of automatic machine learning methods with focus on learning the structure of Bayesian networks and Markov networks
Machine Learning is an exciting topic of programming computers to learn the underlying model automatically when presented with examples. When the number of variables involved in the problem is even moderately large, the modeling problem become intractable thereby requiring the use of probabilistic graphical models such as Bayesian networks (which are directed graphs) and Markov networks (which are undirected graphs). In this seminar we will study recent papers in the top conferences such as ICML and NIPS where different application domains for structure learning are explored.
Linear Algebra, Probability Theory
Ph.D.: This course does not fulfill core area or core course requirements.
M.S.: This course does not fulfill core area or core course requirements.
