Spring 2023 Course Descriptions
CS 135-01 Introduction to Machine Learning
TR 10:30-11:45, Barnum/Dana Hall 104
An overview of methods whereby computers can learn from data or experience and make decisions accordingly. Topics include supervised learning, unsupervised learning, reinforcement learning, and knowledge extraction from large databases with applications to science, engineering, and medicine.
Prerequisite: Comp 15 and COMP/MATH 22 or 61 or consent of instructor. (Comp 160 is highly recommended).