Foundations of pattern recognition algorithms and machines, including statistical and structural methods. Data structures for pattern representation, feature discovery and selection, classification vs. description, parametric and non-parametric classification, supervised and unsupervised learning, the use of contextual evidence, clustering, and small sample-size problems. Programming projects will include handling of pictorial and textual patterns.