Machine Learning

Course Description

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: linear algebra, and basic probability and statistics.

Course Details

Limited Enrollment Details: CS 229 (and STATS 229) are not open to High School Summer College or Horizon Scholar students. For a list of available Computer Science courses, please select "High School" or "Horizon Scholar" in the Student Population section of the Course page.

Prerequisites

Linear Algebra and Basic Probability and Statistics.

Syllabus Link

None available.
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