Trees, SVMs, clustering, and ensemble methods beyond neural nets
Recursive partitioning, Gini impurity, and pruning
Classification by proximity and distance metrics
Maximum margin classifiers and the kernel trick
Probabilistic classification with Bayes' rule
Unsupervised grouping by centroid distance
Bootstrap aggregation and decorrelated tree ensembles
Sequential residual fitting for powerful predictions
Voting, stacking, and combining diverse models