MATH 648 Statistical Learning

This course will provide an introduction to methods in statistical learning that are commonly used to extract important patterns and information from data. Topics include: linear methods for regression and classification, regularization, kernel smoothing methods, statistical model assessment and selection, and support vector machines. Unsupervised learning techniques such as principal component analysis and generalized principal component analysis will also be discussed. The topics and their applications will be illustrated using the statistical programing language R.

3 Credit(s)