| Course: | STAT 34800 |
| Title: | Modern Methods in Applied Statistics |
| Instructor(s): | Matthew Stephens |
| Course Assistant(s): | Lei Sun and Fan Yang |
| Class Schedule: | Sec 01: TR 3:30 PM-4:50 PM in Stuart 101 |
| Office Hours: | |
| Textbook(s): | Hastie, Tibshirani, Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed) |
| Description: | This course covers latent variable models and graphical models; definitions and conditional independence properties; Markov chains, HMMs, mixture models, PCA, factor analysis, and hierarchical Bayes models; methods for estimation and probability computations (EM, variational EM, MCMC, particle filtering, and Kalman Filter); undirected graphs, Markov Random Fields, and decomposable graphs; message passing algorithms; sparse regression, Lasso, and Bayesian regression; and classification generative vs. discriminative. Applications will typically involve high-dimensional data sets, and algorithmic coding will be emphasized. Prerequisites: STAT 34300 |