# STATS 790, Winter 2020

**Statistical Learning**

Prerequisite(s): None but STATS 780 is recommended if students are not already familiar with that material.

The course will cover topics in statistical learning, building on the topics already covered in STATS 780. The core topics are cross-validation, shrinkage methods (ridge regression, the LASSO, etc.), neural networks, gradient boosting, separating hyperplanes, support vector machines, basis expansion and regularization (e.g., smoothing splines, wavelet smoothing, kernel smoothing), generalized additive models, bump hunting, multivariate adaptive regression splines (MARS), self-organizing maps, mixture model-based clustering, ensemble learning, and p>>n problems. For computing, the R software will be used as well as either Julia or Python.

**Instructor: **

**Up-to-date information presented in your classes**

**PLEASE REFER TO MOSAIC FOR THE MOST UP-TO-DATE INFORMATION ON TIMES AND ROOMS**