STA 685 - Advanced Data Models

[4 credit(s)]

Pre-requisites: STA 524 and STA 567, or departmental approval. 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.

Click here for the schedule of courses