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CS 383 Machine Learning 3.0 Credits

This course covers the fundamentals of modern statistical machine learning. Lectures will cover the theoretical foundation and algorithmic details of representative topics including probabilities and decision theory, regression, classification, graphical models, mixture models, clustering, expectation maximization, hidden Markov models, and weak learning.

College/Department: College of Computing and Informatics
Repeat Status: Not repeatable for credit
Prerequisites: CS 260 [Min Grade: D] and (MATH 201 [Min Grade: D] or ENGR 231 [Min Grade: D]) and (MATH 221 [Min Grade: D] or MATH 222 [Min Grade: D]) and (MATH 311 [Min Grade: D] or MATH 410 [Min Grade: D] or ECE 361 [Min Grade: D])

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