Lectures and discussions related to advanced topics and new areas of interest in signal processing: speech, image, and multidimensional processing. Course Information: May be repeated 8 hours in a term to a total of 20 hours. Credit towards a degree from multiple offerings of this course is not given if those offerings have significant overlap, as determined by the ECE department. Prerequisite: As specified each term. It is expected that each offering will have a 500-level course as prerequisite or co-requisite.
Topic: Pattern Recognition. Prerequisite: CS 225, ECE 390 or equivalent programming experience; Math 415 or equivalent; ECE 313, Math 461, Stat 410 or equivalent. Pattern Recognition is concerned with recognition of an unknown given object as belonging to one of a number of classes. Classification is performed by discovering class specific ?patterns? among a range of measurable object features and utilizing these class characteristic features for recognition of unknown objects. The design of a pattern recognition system requires development of four major modules: sensing, feature extraction, decision making, and system performance evaluation. This course will introduce the fundamentals of statistical pattern recognition with examples from several application areas. Techniques for handling multidimensional data of various types and scales along with classification/recognition algorithms will be explained. The course will present competing approaches to exploratory data analysis an