Fall 2017 Course Descriptions
COMP 136-01 Statistical Pattern Recognition
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.