UB - University at Buffalo, The State University of New York Computer Science and Engineering

CSE 674: Advanced Machine Learning

This page refers to the Spring 2017 offering of CSE 674 only. The information on this page does not necessarily apply to every offering of CSE 674.

Spring 2017


Artificial Intelligence/Machine Learning

This course is focused on probabilistic graphical models (PGMs). We will study both directed graphical models (Bayesian networks) and undirected graphical models (Markov Networks, also known as Markov Random Fields). Topics will include methods of representation, independence properties, exact inference algorithms (variable elimination and belief propagation), approximate inference algorithms (variational and Monte Carlo), learning PGMs (parameters and structure) and causality. Relationship of generative and discriminative PGM methods to deep learning is also explored.

CSE 4/574 or equivalent

Ph.D.: None.

M.S.: This course does not fulfill core area (depth) or core course (breadth) requirements.

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