CEN 647 - PATTERN RECOGNITION

PATTERN RECOGNITION
Course Description:
 
Covers basic concepts of pattern recognition systems, application examples, PDF estimation, maximum likelihood estimation, Bayesian estimation, KNN estimation, parzen windows estimation, expectation maximization algorithm, feature reduction, supervised classification, Bayesian classification, discriminant functions, classifier combination, Markov random fields, Artificial neural networks, support vector machines,
 
Textbook(s) and/or Other Required Materials:
 

1. Duda, Heart and Storck, Pattern classification, 2nd edition, 2000
2. Cristopher M. Bishop, Pattern recognition and machine learning, 2006   
3. N. Cristianini and S. Taylor, "An introduction to support vector machines," Cambridge Univ. Press 2000 

Major Topics covered and schedule in weeks:
Recognition systems: 2
Statistical estimation theory: 2
Feature reduction: 2
Supervised Classification: 2
Artificial neural networks: 3
Support vector machines: 3

Evaluation
Attendance                              10%
Projects                                   60%
Midterm exam                          15%
Final exam                               15%