Fall 2022 Course Descriptions

CS 135-01S Introduction to Machine Learning

B. Huang
TR 12:00-1:15, Joyce Cummings Center 270
F+ Block

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).

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