Statistical Data Mining

Catalog description

General statistical techniques for unsupervised and supervised learning, with more emphasis on methodology; topics covered: association rules, outlier detection, PageRank, parametric nonlinear regression; optimization, conventional nonparametric regression methods (including kernel smoothing/regression and smoothing and regression splines), generalized additive models (GAM), multivariate adaptive regression splines (MARS), recursive partitioning and extensions, hierarchical mixture of experts (HME), projection pursuit regression, artificial neural networks (ANN), support vector machine (SVM), and naive Bayes classifier.