Basis expansions and regularization (piecewise polynomials, splines, filtering and feature Extraction).
Smoothing methods (smoothing splines, Kernel smoothing, wavelet smoothing).
Additive models, tree-based methods and boosting methods.
Boosting and additive trees
Support vector machines and flexible discriminants.
Unsupervised learning (association rules, cluster analysis).
Bayesian learning: Bayes' theorem to combine data information with other prior information. Bayesian analysis of conjugate models. Markov Chain Monte Carlo methods for Bayesian computations.