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:
- Duda, Heart and Storck, Pattern classification, 2nd edition, 2000
- Cristopher M. Bishop, Pattern recognition and machine learning, 2006
- 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%