Statistical Learning and Data Mining
Weighted least squares, robust regression, regularization, dimension reduction, nonlinear regression, local regression, generalized additive models, Gaussian process regression, tree-structured regression, support vector machines, classification.
STAT
536
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesSTAT 535 & STAT 624
 TaughtWinter
Course Outcomes: 


STAT 536

This course trains students in using statistical methods for modeling a response variable as a function of explanatory variables. Stat 535 (a prerequisite) covered linear models, and this course attempts to cover the complement set. At a minimum you will learn the derivation, computation, and application of the different methods on data.

Linear Regression

Review Linear Regression Models

Weighted Least Squares

Review Weighted Least Squares, Mixed Models

Bayesian

Bayesian Linear Regression

Measurement

Measurement Error Models

Linear Models

Generalized Linear Models (logistic)

Model Assessment

Model Assessment and Selection

Shrinkage Methods

Shrinkage Methods, Bias-Variance Tradeoff, Subset Selection

Local Regression

Local Regression (splines, smoothers)

GAM

Generalized Additive Models (GAM)

Tree-based Models

Tree-based Models, Random Forests

Boosting

Boosting, Bayesian Adaptive Regression Trees

p >> n

p >> n