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Course: STAT 34800
Title: Modern Methods in Applied Statistics
Instructor(s): Matthew Stephens
Course Assistant(s): Kushal Dey, Lei Sun
Class Schedule: Sec 01: MW 3:00-4:20 PM in Harper Memorial 130
Office Hours:  
Textbook(s): TBA
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 and STAT 34700 or consent of instructor