Introduction to Machine Learning
CS 429 / 529 (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 and STAT **345 and (MATH **314 or MATH **321).
Restriction: admitted to School of Engineering.
Prerequisites / Corequisites
Data Structures and Algorithms II - CS 362
Elements of Mathematical Statistics and Probability Theory - STAT **345
Linear Algebra with Applications - MATH **314
Linear Algebra - MATH **321
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