Spring 2023 Course Descriptions
CS 136-01 Statistical Pattern Recognition
MW 10:30-11:45, Joyce Cummings Center 180
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.