| Line 30: | Line 30: | ||
== Interesting pages in the ECE662 category == | == Interesting pages in the ECE662 category == | ||
*[[ECE662:Glossary_Old_Kiwi|Decision Theory Glossary]] | *[[ECE662:Glossary_Old_Kiwi|Decision Theory Glossary]] | ||
| + | *[[Bayes_Classification:_Experiments_and_Notes_OldKiwi|The effect of adding correlated features]] | ||
*[[Parametric_Estimators_OldKiwi|About Parametric Estimators]] | *[[Parametric_Estimators_OldKiwi|About Parametric Estimators]] | ||
*[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]] | *[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]] | ||
Revision as of 12:07, 2 February 2012
Contents
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 every Spring of even years.
Textbooks
"Introduction to Statistical Pattern Recognition" by K. Fukunaga
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
- 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
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
