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

CSE 674: Advanced Machine Learning

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.

Ph.D.:

None.

M.S.:

This course fulfills one Artificial Intelligence Core Area (Depth) requirement.

CSE 4/574 or equivalent

Course Instances
Semester Section Title Instructor Credit Hours Enrolled
Spring 2017 LEC Advanced Machine Learning Dr. Sargur (Hari) N. Srihari 3 39/97
Spring 2016 LEC Advanced Machine Learning Dr. Sargur (Hari) N. Srihari 3 35/97
Spring 2015 LEC Advanced Machine Learning Dr. Sargur (Hari) N. Srihari 3 55/70
Spring 2014 LEC Advanced Machine Learning Dr. Sargur (Hari) N. Srihari 3 40/70
Spring 2012 LEC Advanced Machine Learning Dr. Sargur (Hari) N. Srihari 3 1/10
Spring 2012 LEC Advanced Machine Learning Dr. Sargur (Hari) N. Srihari 3 27/30
Spring 2011 LEC Advanced Machine Learning Dr. Sargur (Hari) N. Srihari 3 0/10
Spring 2011 LEC Advanced Machine Learning Dr. Sargur (Hari) N. Srihari 3 10/30
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