Course Syllabus ECE 637: Pattern Recognition

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Oct 15, 2013 (3 years and 21 days ago)


Course Syllabus

ECE 637: Pattern Recognition


Electrical and Computer Engineering

Course Number:


Course Title:

Pattern Recognition

Credit Units:


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, Line
ar 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 c
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 recog
, 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
art algorithms used in pattern recognition research

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
neering systems.

c) Ability to apply the appropriate industry practices, emerging technologies, state
design techniques, software tools, and research methods of solving electrical and computer
engineering problems.

d) ability to use the appropri
ate state
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 appropri
ate 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 withou
t 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:

wen Chen

January 12, 2003