Principles and design of pattern recognition systems. Statistical classifiers: discriminant functions; bayes, minimum risk, k-nearest neighbors, perceptrons. Clustering and estimation; criteria; k-means, fuzzy, hierarchal, graph- theoretic, simulated and determininstic annealing; maximum likelihood and bayesian methods: nonparametric methods. Overview of applications.