|Title:||Modern Methods in Applied Statistics|
|Course Assistant(s):||Lei Sun and Fan Yang|
|Class Schedule:||Sec 01: TR 3:30 PM-4:50 PM in Stuart 101|
|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