EEL 4822 - Pattern Recognition

Credits: 3

Prerequisites: MTG 4930
Course Description: The main goal of this course is to underlie the principles of pattern recognition and the methods of machine intelligence used to develop and deploy pattern recognition applications in the real world. The algorithms to be presented include feature extraction and selection, clustering, artificial neural networks, support vector machines, rule-based algorithms, fuzzy logic, genetic algorithms, and others. Case studies drawn from actual machine intelligence applications will be used to illustrate how methods such as pattern detection and classification, signal taxonomy, machine vision, anomaly detection, data mining, and data fusion are applied in realistic problem environments.