Course Description: Theory and applications of statistical, neural and syntactical pattern recognition. Topics include Bayesian decision theory, discriminant functions, parametric and non-parametric techniques, multilayer neural networks, decision trees, algorithm-independent machine learning and unsupervised learning and clustering. Scheduled summer semesters of odd years.
