INEL 5995A: Pattern Recognition

Catalog Data:  INEL 5995A – An introduction to the field of Pattern Recognition: Statistical Decision Making, Nonparametric Decision Making, Clustering, Artificial Neural Networks, Learning Techniques, Evaluation of a Classification Rule, and Image Analysis.
Textbook: 
 Earl Gose, Richard Johnsonbaugh, Steve Jost, Pattern Recognition and Image Analysis, Prentice Hall, 1996.
References:
 Morton Nadler, Eric P. Smith, Pattern Recognition Engineering, John Wiley and Sons, 1993.Keinosuke Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press Inc., 1990.
Richard O. Duda, Peter E. Hart, Pattern Classification and Scene Analysis, John Wiley and Sons, 1973.
Sergio Theodoridis, Konstantinos Koutroumbas, Pattern Recognition, Academic Press Inc., 1999.
Geoffrey J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition, John Wiley and Sons, 1992.
Jurgen Schurmann, Pattern Classification, John Wiley and Sons, 1996.
Menahem Friedman, Abraham Kandel, Introduction to Pattern Recognition, World Scientific Publishing Co., 1999.
Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997.
Luc Devroye, Laszlo Gyorfi, Gabor Lugosi, A Probabilistic Theory of Pattern Recognition, Springer, 1996.
Simon Haykin, Neural Network, Macmillan College Publishing Company, 1994.
Robert J. Schalkoff, Artificial Neural Network, McGraw-Hill, 1997.
Christofer M. Bishop, Neural Network for Pattern Recognition, Clarendon Press-Oxford, 1995.
Martin T. Hagan, Howard B. Demuth, Mark Beale, Neural Network Design, PWS Publishing Company, 1996.
Goals:
 		Introduce the students to the fundamental concepts of pattern recognition, provide them the ability to design pattern recognition’s based algorithms to analyze signals and images.
Topics:
 1. Introduction (2 classes)
2. Matrix Algebra Review (4 classes)
3. Probability Review (4 classes)
4. Statistical Decision Making (6 classes)
5. Nonparametric Decision Making (5 classes)
6. Clustering (5 classes)
7. Artificial Neural Network (6 classes)
8. Processing of Waveforms and Images Analysis (7 classes)
9. Advance Topics (4 classes)
10. Test (2 classes)