EAS 509LEC - Statistical Learning and Data Mining II

An introduction to the mathematical theory and computational methodology at the heart of statistical learning. Using a Bayesian paradigm, this second semester considers unsupervised learning, including dimension reduction, clustering, Gaussian mixtures methods, graph models, and model averaging; parametric and non-parametric regression, and Gaussian Process regression. R will be used as a programming language. Prerequisite experience: EAS 508 or equivalent, or permission of instructor.

Credits: 3

Requisites: Pre-Requisite: EAS 508 or permission of instructor.