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Introduction to Machine Learning

CS 4375

Erik Jonsson School of Engineering and Computer Science

Algorithms for creating computer programs that can improve their performance through learning. Topics include: cross-validation, decision trees, neural nets, statistical tests, Bayesian learning, computational learning theory, instance-based learning, reinforcement learning, bagging, boosting, support vector machines, Hidden Markov Models, clustering, and semi-supervised and unsupervised learning techniques. 3 credit hours.

Prerequisites: (CS 3341 or SE 3341 or (Data Science major and STAT 3355)) and (CE 3345 or CS 3345 or SE 3345) or equivalent.

Offering Frequency: Each year

Grades: 4,012

Median GPA: A-

Mean GPA: 3.223

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