Fall 2017 Course Descriptions

COMP 136-01 Statistical Pattern Recognition

R. Khardon
MW 1:30-2:45, Barnum/Dana Hall 104
G+ Block

Statistical foundations and algorithms for machine learning with a focus on Bayesian modeling. Topics include: classification and regression problems, regularization, model selection, kernel methods, support vector machines, Gaussian processes, Graphical models.

Prerequisite: Prerequisites: MATH 13 or 42; MATH 46 or 70; EE 104 or MATH 162; COMP 40 or COMP 105 or a programming course using Matlab. COMP 135, or COMP 131 are recommended but not required. Or permission of instructor.

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