ECE 637: Pattern Recognition

Course Syllabus ECE 637: Pattern Recognition

Department: Electrical and Computer Engineering Course Number: ECE637 Course Title: Pattern Recognition Credit Units: 3

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.

Prerequisites 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. Program.

Prepared by: Xue-wen Chen January 12, 2003