Machine learning and pattern classification are fundamental blocks in the design of an intelligent system. This course will introduce fundamentals of machine learning and pattern classification concepts, theories, and algorithms. Topics covered include: Bayesian decision theory, linear discriminant functions, multilayer neural networks, classifier evaluation, and an introduction to unsupervised clustering/grouping, self-organization and evolutionary computation. Lect: 3 hrs./Lab: 1 hr. Prerequisite: MTH 514 Course Weight: 1.00 Billing Units: 1
