University at Buffalo 3 credits Engineering & Applied Sciences
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.
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