This course in machine learning focuses on higher-dimensional data and a broader class of nonlinear function approximation approaches. Topics include: optimization approaches (constrained optimization, hessians, matrix solutions), kernel machines, neural networks, dimensionality reduction, latent variables, feature selection, more advanced methods for assessing generalization (cross-validation, bootstrapping), introduction to non-iid data and missing data. Credit cannot be obtained for both CMPUT 367 and CMPUT 466. Prerequisites: CMPUT 204 and 267; one of MATH 115, 118, 136, 146, or 156.