Pattern Recognition

Course content

An introduction to the machine recognition of 1, 2 or higher dimensional patterns. Statistical and linguistic approaches. Survey of application areas. Bayes Decision Theory. Decision boundaries, classifiers, and discriminant functions. Estimation of parameters. Clustering. Feature selection. Structural approaches to P.R.. Neural networks recognizers. Applications.