STA 685

Advanced Data Models

This course introduces various methods of modern, computationally-based methods for exploring and drawing inferences from data. After a brief review of probability and inference, the course covers resampling methods, non-parametric regression, prediction, and dimension reduction and clustering. Specifically topics include: tree-based methods, boosting, ensemble learning, forests, neural networks, support vector machines, bootstrap, cross-validation, smoothing methods such as kernels, local regression, splines, smoothing in likelihood models, density estimation, shrinkage methods (ridge regression, lasso), longitudinal data analysis and high dimensional problems.

Units: 4.0