Courses

ECE 598 NA
Pattern Recognition

Official Description	Subject offerings of new and developing areas of knowledge in electrical and computer engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. May be repeated in the same or separate terms if topics vary.
Hours	0 to 4 hours.
Subject Area	Graduate Seminar and Thesis Research
Course Prerequisites	Credit in MATH 415 Credit in ECE 313 or MATH 461 or STAT 410 Credit in CS 225 or ECE 290
Course Directors	Narendra Ahuja
Description	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 and classifier design so students can make judicious choices when confronted with real pattern recognition problems.
Credit	4 hours
Topics	Statistical Decision Theory Parameter Estimation Curse of Dimensionality Component Analysis and Discriminants Nonparametric Techniques Linear Discriminant Functions Support Vector Machines Decision Trees Neural Networks Stochastic and Nonmetric Methods Error Rate Estimation, Bagging, Boosting Classifier Combination Feature Selection Unsupervised Learning and Clustering Applications
Course Prerequisites	MATH 415 or equivalent; ECE 313, MATH 461 or STAT 410 or equivalent; and CS 225, ECE 390, or equivalent programming experience.
Texts	Duda, Hart, and Stork, Pattern Classification, 2nd Edition, Wiley, 2001. Recommended: A list of books will be given to students for periodic reading assignments.