Classification: Gaussian discriminant analysis (linear and quadratic), naive Bayes and Bayesian naive Bayes, nearest neighbor classifiers, logistic regression, support vector machines (SVMs), kernel trick, multi-class algorithms, decision trees
Regression: linear (ordinary) least squares, robust linear, ridge, kernel, Lasso, trees
Density Estimation: GMM and the EM algorithm, kernel methods
Clustering: k-means/medoids connection to EM for GMMs, spectral clustering, hierar-chical clustering
Dimensionality Reduction: PCA, kernel PCA, MDS, Isomap, LLE, Laplacian eigenmaps,