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== Main Course Topics == | == Main Course Topics == | ||
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* [[About Pattern Recognition]] | * [[About Pattern Recognition]] | ||
* [[Bayes_Decision_Theory]] | * [[Bayes_Decision_Theory]] | ||
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== Semester/Instructor specific pages == | == Semester/Instructor specific pages == | ||
| + | *[[2012_Spring_ECE_662_Boutin|Spring 2012, Prof. Boutin]] | ||
*[[2010_Spring_ECE_662_mboutin|Spring 2010, Prof. Boutin]] | *[[2010_Spring_ECE_662_mboutin|Spring 2010, Prof. Boutin]] | ||
*[[ECE662:BoutinSpring08_OldKiwi|Spring 2008, Prof. Boutin]] | *[[ECE662:BoutinSpring08_OldKiwi|Spring 2008, Prof. Boutin]] | ||
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* [["Pattern Recognition and Neural Networks" by Brian Ripley_OldKiwi]] | * [["Pattern Recognition and Neural Networks" by Brian Ripley_OldKiwi]] | ||
* [["Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar_OldKiwi]] | * [["Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar_OldKiwi]] | ||
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[[ECE|Back to ECE]] | [[ECE|Back to ECE]] | ||
[[Meta Course List|Back to Course List]] | [[Meta Course List|Back to Course List]] | ||
Revision as of 04:53, 19 December 2011
Contents
ECE 662: Statistical Pattern Recognition and Decision Making Processes
Click here to view a list of all pages in the ECE662 category.
ECE662 is a course that is cross-linked with CS. It is taught every Spring of even years.
Textbooks
"Introduction to Statistical Pattern Recognition" by K. Fukunaga_OldKiwi
Peer Legacy
Share advice with future students regarding ECE662 on this page.
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
- 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
Semester/Instructor specific pages
Other References
- "Pattern Classification" by Duda, Hart, and Stork_OldKiwi
- "Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler_OldKiwi
- "Pattern Recognition and Neural Networks" by Brian Ripley_OldKiwi
- "Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar_OldKiwi
