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[[Category:ECE662]] | [[Category:ECE662]] | ||
| + | [[Category:pattern recognition]] | ||
| + | [[Category:decision theory]] | ||
| − | = | + | |
| − | + | <center><font size= 4> | |
| + | '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' | ||
| + | </font size> | ||
| + | |||
| + | (cross-listed with computer science as CS662) | ||
| + | |||
| + | </center> | ||
| + | |||
| + | ---- | ||
| + | ---- | ||
| + | This course was previously developed and taught by Professor [https://engineering.purdue.edu/ECE/People/profile?resource_id=3088 Keinosuke Fukunaga]. | ||
| + | |||
| + | Since 2006, it is taught by [[user:mboutin|Prof. Boutin]] every Spring of even years. | ||
==Textbooks== | ==Textbooks== | ||
| − | [["Introduction to Statistical Pattern Recognition" by K. Fukunaga_OldKiwi]] | + | [["Introduction to Statistical Pattern Recognition" by K. Fukunaga_OldKiwi|"Introduction to Statistical Pattern Recognition" by K. Fukunaga]] |
== Peer Legacy == | == Peer Legacy == | ||
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]]. | ||
| − | == | + | == Slectures and Lecture Notes == |
| + | *[[ECE662_Pattern_Recognition_Decision_Making_Processes_Spring2008_sLecture_collective|Spring 2008, Prof. Boutin]], notes collectively written by the students in the class. | ||
| + | *[[2014_Spring_ECE_662_Boutin_Statistical_Pattern_recognition_slectures|The Boutin Lectures on Statistical Pattern Recognition]], Multilingual Slectures by Students in the Spring 2014 Class of ECE662 | ||
| − | + | == Some Course Topics == | |
| − | + | * [[Bayes_Decision_Theory]] | |
| − | * [[ | + | |
* [[Fisher Linear Discriminant]] | * [[Fisher Linear Discriminant]] | ||
| − | |||
* [[Feature Extraction]] | * [[Feature Extraction]] | ||
| − | |||
| − | |||
* [[Artificial Neural Networks]] | * [[Artificial Neural Networks]] | ||
* [[Support Vector Machines]] | * [[Support Vector Machines]] | ||
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== Interesting pages in the ECE662 category == | == Interesting pages in the ECE662 category == | ||
| − | *[[ | + | *[[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]] | |
| + | *[[Fisher_discriminant_under_nonlinear_data|Fisher linear discriminant can be used for non-linearly separable data too!]] | ||
| + | *[[ANN_Simulink_examples_ece662_Sp2010|A jump start on using Simulink to develop a ANN-based classifier]] | ||
| + | *[[KNN-K_Nearest_Neighbor_OldKiwi|The K Nearest Neighbor Algorithm]] | ||
| + | *[[MLE_Examples:_Binomial_and_Poisson_Distributions_OldKiwi|MLE example: binomial and poisson distributions]] | ||
| + | *[[MLE_Examples:_Exponential_and_Geometric_Distributions_OldKiwi|MLE example: exponential and geometric distributions]] | ||
| + | *[[Bayes_Decision_Rule_Old_Kiwi|Video illustrating the decision boundary for normally distributed features]] | ||
| + | ::<youtube>wzJkaATyitA</youtube> | ||
== Semester/Instructor specific pages == | == Semester/Instructor specific pages == | ||
| + | *[[2016_Spring_ECE_662_Boutin|Spring 2016, Prof. Boutin]] | ||
| + | *[[2014_Spring_ECE_662_Boutin|Spring 2014, Prof. Boutin]] | ||
| + | *[[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]] | ||
| − | ==References== | + | ==Other References== |
| − | * [["Pattern Classification" by Duda, Hart, and Stork_OldKiwi]] | + | * [["Pattern Classification" by Duda, Hart, and Stork_OldKiwi|"Pattern Classification" by Duda, Hart, and Stork]] |
| − | * [["Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler_OldKiwi]] | + | * [["Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler_OldKiwi|"Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler]] |
| − | * [["Pattern Recognition and Neural Networks" by Brian Ripley_OldKiwi]] | + | * [["Pattern Recognition and Neural Networks" by Brian Ripley_OldKiwi|"Pattern Recognition and Neural Networks" by Brian Ripley]] |
| − | * [["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|"Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar]] |
| − | + | ||
| − | + | ||
| − | + | ||
---- | ---- | ||
| + | [[ECE|Back to ECE]] | ||
| + | |||
[[Meta Course List|Back to Course List]] | [[Meta Course List|Back to Course List]] | ||
Latest revision as of 08:07, 11 January 2016
ECE662: Statistical Pattern Recognition and Decision Making Processes
(cross-listed with computer science as CS662)
This course was previously developed and taught by Professor Keinosuke Fukunaga.
Since 2006, it is taught by Prof. Boutin every Spring of even years.
Contents
Textbooks
"Introduction to Statistical Pattern Recognition" by K. Fukunaga
Peer Legacy
Share advice with future students regarding ECE662 on this page.
Slectures and Lecture Notes
- Spring 2008, Prof. Boutin, notes collectively written by the students in the class.
- The Boutin Lectures on Statistical Pattern Recognition, Multilingual Slectures by Students in the Spring 2014 Class of ECE662
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
- Spring 2016, Prof. Boutin
- Spring 2014, Prof. Boutin
- 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
