EAS 506LEC - Statistical Data Mining I

This course presents statistical models for data mining, inference and prediction. The focus will be on supervised learning, which concerns outcome prediction from input data. Students will be introduced to a number of methods for supervised learning, including: linear and logistic regression, shrinkage methods, lasso, partial least squares, tree-based methods, model assessment and selection, model inference and averaging, and neural networks. Computational applications will be presented using R and high dimensional data to reinforce theoretical concepts. This course is the same as STA 545 and CDA 541, and course repeat rules will apply. Students should consult with their major department regarding any restrictions on their degree requirements.

Credits: 3

Requisites: Pre-Requisite: MTH 142 (or MTH 138 and MTH 139)And MTH 309 And STA 511; or Engineering, Biostatistics or Mathematics Major.