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Share advice with future students regarding ECE662 on [[Peer_Legacy_ECE662|this page]]. | Share advice with future students regarding ECE662 on [[Peer_Legacy_ECE662|this page]]. | ||
| − | == | + | == Lecture Notes == |
| − | * [[ | + | *[[ECE662_Pattern_Recognition_Decision_Making_Processes_Spring2008_sLecture_collective|Spring 2008, Prof. Boutin]], notes collectively written by the students in the class. |
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| + | |||
| + | == Some Course Topics == | ||
* [[Bayes_Decision_Theory]] | * [[Bayes_Decision_Theory]] | ||
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* [[Fisher Linear Discriminant]] | * [[Fisher Linear Discriminant]] | ||
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* [[Feature Extraction]] | * [[Feature Extraction]] | ||
| − | |||
| − | |||
* [[Artificial Neural Networks]] | * [[Artificial Neural Networks]] | ||
* [[Support Vector Machines]] | * [[Support Vector Machines]] | ||
Revision as of 07:27, 11 April 2013
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 Prof. Boutin 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.
Lecture Notes
- Spring 2008, Prof. Boutin, notes collectively written by the students in the class.
Some Course Topics
- Bayes_Decision_Theory
- Fisher Linear Discriminant
- Feature Extraction
- 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
- MLE example: binomial and poisson distributions
- MLE example: exponential and geometric distributions
- Video illustrating the decision boundary for normally distributed features
Semester/Instructor specific pages
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
