ECE662 Pattern Recognition and Decision Making Processes
Instructor: Prof. S.
Lectures: MW 5:45–7 pm in SL 165
Textbook: Introduction to Statistical Pattern Recognition, 2nd edition, by Keinosuke Fukunaga, Academic Press, 1990.
Prerequisites: ECE 302 (Probabilistic Methods in Electrical Engineering) or equivalent
Description: Introduction to the basic concepts
and various approaches of pattern recognition and decision
making process. The topics include various classifier
designs, evaluation of classifiability, learning machines,
feature extraction and modeling.
- Tentative Outline:
- Introduction (Week 1)
- A. Problems in the decision making processes
- B. Mathematical formulation
- Pattern recognition and learning machines
- Review of probability theory and linear algebra (Week 2)
- Bayes classification (Week 3)
- Parametric classifier design (Week 4)
- Nonparametric design (Weeks 5, 6)
- Estimation of classifiability (Weeks 7, 8)
- Classifier evaluation (Week 9)
- Learning algorithms (Week 10)
- Data Structure Analysis
- Feature extraction for signal representation (Week 11)
- Feature extraction for classification (Weeks 12, 13)
- Clustering (Week 14)
- Modeling and validity tests (Week 15)
(Updated August 13, 2013)
Homework Assignments (Updated
November 02, 2004)
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