MATH 457 - Intro to Statistical Learning

Credits: 4

Statistical learning refers to a set of tools for modeling and understanding complex datasets. This course covers such topics as regression, classification, resampling, model selection, regularization, tree?based methods, support vector machine, principal components analysis and clustering methods. It concentrates more on the applications of the methods and provides students with valuable hands?on experience. Prerequisites: C or better in linear algebra, Math 304 (or equivalent), computing, Math 329 (or equivalent), and inferential statistics and simple linear regression, Math 448 (or equivalent), or consent of instructor. Offered regularly.