ECE 532: Theory and Applications of Pattern Recognition

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ECE 532 - Theory and Applications of Pattern Recognition

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Catalog Description

532 Theory and Applications of Pattern Recognition. (Crosslisted with Comp Sci, ME.) Even Yrs, II; 3 cr (P-A). Pattern recognition systems and components; decision theories and classification; discriminant functions; supervised and unsupervised training; clustering; feature extraction and dimensional reduction; sequential and hierarchical classification; applications of training, feature extraction, and decision rules to engineering problems. P: ECE 331 or Math 431 or cons inst.

Course Prerequisite(s)

ECE 331
Math 431

Prerequisite knowledge and/or skills

Prior exposure to linear algebra and matrix operations.
Prior exposure to the probabilities theory and statistics.

Textbook(s) and/or other required material

R. Duda,P. Hart and D. Stork Pattern Classification and Scene Analysis, John Wiley, 2001.
Lecture notes

Course objectives

This course covers the theory and methods for learning from data, with an emphasis on pattern classification.

Topics covered

Bayesian decision theory
Nonparametric methods
Linear discriminant functions
Decision Trees
Learning theory

Class/laboratory schedule

Three 50-minute lectures of two 75-minute lectures per week.

Contribution of course to meeting the professional component

This course contributes primarily to the students' knowledge of engineering topics, but does not provide design experience.

The following statement indicates which of the following considerations are included in this course: economic, environmental, ethical, political, societal, health and safety, manufacturability, sustainability.

economic, societal, health and safety

Relationship of course to undergraduate degree program objectives and outcomes

This course primarily serves students in the department. The information below describes how the course contributes to the undergraduate program objectives.

knowledge in the basic techniques of mathematics and the physical sciences basic skill in methods of design and analysis across a broad range of electrical and computer engineering areas strong skills in problem solving, leadership, and communication the ability to make thoughtful, well-informed career choices

Assessment of student progress toward course objectives

homework assignments
midterm exams
final exam
projects

Person(s) who prepared this description

Robert D. Nowak

Copyright 2007 The Board of Regents of the University of Wisconsin System
Date last modified: 18-Jul-2007
Content by: ece@engr.wisc.edu
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