AMCS 229 Machine Learning



Topics: statistical pattern recognition, linear and non-linear regression, non-parametric ethods, exponential family, GLIMs, 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.
Course period02/13/10 → …
Course level200