Revision as of 12:59, 8 March 2012

ECE 662: Statistical Pattern Recognition and Decision Making Processes (cross-listed with computer science as CS662)

Click here to view a list of all pages in the ECE662 category.

This course was previously developed and taught by Professor Keinosuke Fukunaga.

Since 2006, it is taught by Prof. Boutin every Spring of even years.

Textbooks

"Introduction to Statistical Pattern Recognition" by K. Fukunaga

Peer Legacy

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Main Course Topics

• About Pattern Recognition
• Bayes_Decision_Theory
• Discriminant Functions
• Fisher Linear Discriminant
• Bayesian Decision Theory for Normally Distributed Features
• Feature Extraction
• Density Estimation
• Linear classifiers
• Artificial Neural Networks
• Support Vector Machines
• Clustering
• Decision Trees

Interesting pages in the ECE662 category

• Decision Theory Glossary
• The effect of adding correlated features
• About Parametric Estimators
• Bayes rule under severe class imbalance
• Fisher linear discriminant can be used for non-linearly separable data too!
• A jump start on using Simulink to develop a ANN-based classifier
• The K Nearest Neighbor Algorithm
• Video illustrating the decision boundary for normally distributed features

Semester/Instructor specific pages

• Spring 2012, Prof. Boutin
• Spring 2010, Prof. Boutin
• Spring 2008, Prof. Boutin

Other References

• "Pattern Classification" by Duda, Hart, and Stork
• "Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler
• "Pattern Recognition and Neural Networks" by Brian Ripley
• "Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar

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