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CAAM 37710 - Machine Learning

Description
This course provides hands-on experience with a range of contemporary machine learning algorithms, as well as an introduction to the theoretical aspects of the subject. Topics covered include: the PAC framework, Bayesian learning, graphical models, clustering, dimensionality reduction, kernel methods including SVMs, matrix completion, neural networks, and an introduction to statistical learning theory.
Credits
100
Recent Professors
Schedule Planner
Recent Semesters
Winter 2021, Spring 2019
Offered
TuTh, MW, W, Th, F
Avg. Class Size
28
Avg. Sections
3