STAT 536
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Statistical Learning and Data Mining
Statistics
College of Computational, Mathematical & Physical Sciences
Course Description
Multiple linear regression, nonlinear regression, local regression, penalized regression, generalized additive models, logistic regression, discriminant analysis, tree-structured regression, support vector machines, neural networks.
When Taught
Fall
Min
3
Fixed/Max
3
Fixed
3
Fixed
0
Other Prerequisites
Departmental consent
Title
Model Assessment
Learning Outcome
Model Assessment and Selection
Title
Linear Regression
Learning Outcome
Review Linear Regression Models
Title
Boosting
Learning Outcome
Boosting, Bayesian Adaptive Regression Trees
Title
Measurement
Learning Outcome
Measurement Error Models
Title
Bayesian
Learning Outcome
Bayesian Linear Regression
Title
Tree-based Models
Learning Outcome
Tree-based Models, Random Forests
Title
p >> n
Learning Outcome
p >> n
Title
Weighted Least Squares
Learning Outcome
Review Weighted Least Squares, Mixed Models
Title
Linear Models
Learning Outcome
Generalized Linear Models (logistic)
Title
Local Regression
Learning Outcome
Local Regression (splines, smoothers)
Title
GAM
Learning Outcome
Generalized Additive Models (GAM)
Title
Shrinkage Methods
Learning Outcome
Shrinkage Methods, Bias-Variance Tradeoff, Subset Selection
Title
STAT 536
Learning Outcome
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.
