ETCS 4200 - Maching Learning

This course introduces the fundamental concepts and techniques underlying modern machine learning systems. Topics covered may include logic and knowledge representation, supervised and unsupervised learning, decision making in the presence of uncertainty, decision trees, graph algorithms, linear and logistic regression, genetic algorithms, probabilistic classifiers, problem solving and search, natural language processing, neural networks, support vector machines, principal component analysis, and other topics. Lab activities will focus on the design and implementation of working ML systems.

Credits: 3

Prereq: ETEC 3401
Lecture hours: 2
Lab hours: 3
Course/lab fee: ENGT