Statistical Learning and Classification

Catalog description

Using the foundational knowledge built in MAT 241 and MAT 300, we continue our study of statistical models. This course moves beyond regression and into classification models, mixed models, and unsupervised learning. This course also emphasizes cross-validation as an important method for approximating test error and analyzing the utility of a model. This course covers discriminant analysis, k nearest neighbors, tree-based methods (bagging, boosting, and random forests), support vector machines, and neural networks.