Spring 2024 Course Descriptions

CS 136-01 Statistical Pattern Recognition

M. Hughes
TR 1:30-2:45, Room To Be Announced
H+ 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 166; CS 40 or CS 105 or a programming course using Matlab. CS 135, or CS 131 are recommended but not required. Or permission of instructor.

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