CS 5350 - Machine Learning

Description
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. Enrollment Requirements: Prerequisites: "C-" or better in (CS 3500 AND CS 3190).
Credits
3
Recent Professors
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Recent Semesters
Spring 2020, Fall 2019, Spring 2019, Fall 2018, Spring 2018
Offered
TuTh, MW, TH
Avg. Class Size
95
Avg. Sections
1