Spring 2021 STAT 34800

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