CS 229:
Machine Learning (STATS 229)
Topics: statistical pattern recognition, linear and nonlinear regression, nonparametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, 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.
Terms: Aut, Spr

Units: 34

Grading: Letter or Credit/No Credit