Decision Theory, ROC Curves, Likelihood Ratio Test, Linear and Quadratic Discriminants. Template‐based Recognition, Feature Extraction, Eigenvector and Multilinear Analysis. Training Methods, Maximum Likelihood and Bayesian Parameter Estimation. Classification techniques: k‐nn, LVQ, SVM, decision tree, ANN, CNN, GAN. Clustering techniques: k‐means, VQ, dendrogram, gap statistics. Applications: image analysis, computer vision, speech analysis, man and machine diagnostics, person identification, spam filtering, industrial inspection, financial data analysis and forecast, and genetics.