• UNM

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.

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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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