Introduction to Machine Learning
CS 529 / 429 (3)Introduction to principles and practice of systems that improve performance through experience. Topics include statistical learning framework, supervised and unsupervised learning, Bayesian analysis, time series analysis, reinforcement learning, performance evaluation and empirical methodology; design tradeoffs.
Prerequisite: 362 or 530 or 561.
Prerequisites / Corequisites
Data Structures and Algorithms II - CS 362
Geometric and Probabilistic Methods in Computer Science - CS 530
Algorithms/Data Structure - CS 561
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Office of the Registrar
MSC11 6325
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