ECE 637

Course Syllabus
ECE 637: Pattern Recognition
Course Number:
Course Title:
Credit Units:
Electrical and Computer Engineering
Pattern Recognition
Course Description
Pattern recognition techniques are used to design automated systems that improve their own
performance through experience. This course covers the methodologies, technologies, and
algorithms of statistical pattern recognition from a variety of perspectives. Topics including
Bayesian Decision Theory, Estimation Theory, Linear Discrimination Functions, Nonparametric
Techniques, Support Vector Machines, Neural Networks, Decision Trees, and Clustering
Algorithms etc. will be presented.
Students taking this course should have graduate standing in electrical and computer
engineering. Specifically, students should be familiar with linear algebra, probability, random
process, and statistics (e.g., ECE 650 or its equivalent). In addition, programming experience
(MATLAB/C/C++) will be helpful.
Text Book
Required: Duda, Hart and Stork, Pattern Classification, Second Edition, Wiley, 2001.
Useful supplementary books:
T.M. Mitchell, Machine learning, Mc Graw-Hill, New York, 1997.
S. Theodoridis, K. Koutroumbas, Pattern recognition, Academic Press, 1999.
Course Objectives
After completing this course, the students should be able to:
1. Understand basic concepts in pattern recognition
2. Gain knowledge about state-of-the-art algorithms used in pattern recognition research
3. Understand pattern recognition theories, such as Bayes classifier, linear discriminant analysis.
4. Apply pattern recognition techniques in practical problems.
Topics Covered/Course Outline
1. Bayesian Decision Theory
2. Estimation Theory
3. EM algorithms and HMM
4. Nonparametric Techniques
5. Linear Discriminant Functions
6. Support vector Machine
7. Neural Networks
8. Stochastic Learning
9. Algorithm Independent Learning
10. Unsupervised Learning
Relationship to Program Outcomes
This course supports the achievement of the following outcomes:
a) Ability to apply knowledge of advanced principals to the analysis of electrical and computer
engineering problems.
b) Ability to apply knowledge of advanced techniques to the design of electrical and computer
engineering systems.
c) Ability to apply the appropriate industry practices, emerging technologies, state-of-the-are
design techniques, software tools, and research methods of solving electrical and computer
engineering problems.
d) ability to use the appropriate state-of-the art engineering references and resources, including
IEEE research journals and industry publications, needed to find the best solutions to electrical
and computer engineering problems.
e) Ability to communicate clearly and use the appropriate medium, including written, oral, and
electronic communication methods.
f) Ability to maintain life-long learning and continue to be motivated to learn new subject.
g) Ability to learn new subjects that are required to solve problems in industry without being
dependent on a classroom environment.
h) Ability to be competitive in the engineering job market or be admitted to an excellent Ph.D.
Prepared by:
Xue-wen Chen
January 12, 2003