Course: STAT 34800
Title: Modern Methods in Applied Statistics
Instructor(s): Matthew Stephens
Course Assistant(s): Wei Kuang, Jason Willwerscheid
Class Schedule: Section 1: TR 4:20 PM-5:40 PM (Remote)
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
