Multiple linear regression, nonlinear regression, local regression, penalized regression, generalized additive models, logistic regression, discriminant analysis, tree-structured regression, support vector machines, neural networks.
STAT 536
This course trains students in using statistical methods for modeling a response variable as a function of explanatory variables. Stat 535 covers 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.