Course Detail
Units:
3.0
Course Components:
Lecture
Enrollment Information
Enrollment Requirement:
Prerequisites: "C-" or better in (CS 3500 AND (MATH 2270 OR MATH 2250) AND (CS 3130 OR ECE 3530)) AND (Full Major status in Computer Science OR Computer Engineering).
Corequisites: "C-" or better in (CS 4150 OR CS 3100).
Description
Meets with CS 6350. This course covers techniques for developing computer programs that can acquire new knowledge automatically or adapt their behavior over time. Topics include several algorithms for supervised and unsupervised learning, decision trees, online learning, linear classifiers, empirical risk minimization, computational learning theory, ensemble methods, Bayesian methods, clustering and dimensionality reduction.