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[[Category:ECE662]] | [[Category:ECE662]] | ||
| + | [[Category:ECE]] | ||
| + | [[Category:pattern recognition]] | ||
| + | |||
| + | =[[ECE662]], Spring 2008, Prof. [[user:mboutin|Boutin]]= | ||
==Introduction== | ==Introduction== | ||
| − | This is the page for the course ECE662: Pattern Recognition and Decision Making processes. | + | This is the page for the Spring 2008 edition of the course [[ECE662|ECE662: Pattern Recognition and Decision Making processes]]. |
===General Course Information=== | ===General Course Information=== | ||
| − | * Instructor: Mimi Boutin | + | * Instructor: [[user:mboutin|Mimi Boutin]] |
* Office: MSEE342 | * Office: MSEE342 | ||
* Email: mboutin at purdue dot edu | * Email: mboutin at purdue dot edu | ||
* Class meets Tu,Th 9-10:15am in ME118 | * Class meets Tu,Th 9-10:15am in ME118 | ||
* Office hours: Monday, Thursday 4-5pm | * Office hours: Monday, Thursday 4-5pm | ||
| + | * TA hours: Thursday, 11:45 am-12:45 pm , EE 306 | ||
===Course Website=== | ===Course Website=== | ||
| Line 34: | Line 39: | ||
* [[Lecture 11 - Fischer's Linear Discriminant again_Old Kiwi]] | * [[Lecture 11 - Fischer's Linear Discriminant again_Old Kiwi]] | ||
* [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi]] | * [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi]] | ||
| − | * [[Lecture 13 - Kernel function for SVMs and ANNs introduction_Old Kiwi]] | + | * [[Lecture 13 - Kernel function for SVMs and ANNs introduction_Old Kiwi]] |
* [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi]] | * [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi]] | ||
* [[Lecture 15 - Parzen Window Method_Old Kiwi]] | * [[Lecture 15 - Parzen Window Method_Old Kiwi]] | ||
| Line 41: | Line 46: | ||
* [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi]] | * [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi]] | ||
* [[Lecture 19 - Nearest Neighbor Error Rates_Old Kiwi]] | * [[Lecture 19 - Nearest Neighbor Error Rates_Old Kiwi]] | ||
| + | * [[Lecture 20 - Density Estimation using Series Expansion and Decision Trees_Old Kiwi]] | ||
| + | * [[Lecture 21 - Decision Trees(Continued)_Old Kiwi]] | ||
| + | * [[Lecture 22 - Decision Trees and Clustering_Old Kiwi]] | ||
| + | * [[Lecture 23 - Spanning Trees_Old Kiwi]] | ||
| + | * [[Lecture 24 - Clustering and Hierarchical Clustering_Old Kiwi]] | ||
| + | * [[Lecture 25 - Clustering Algorithms_Old Kiwi]] | ||
| + | * [[Lecture 26 - Statistical Clustering Methods_Old Kiwi]] | ||
| + | * [[Lecture 27 - Clustering by finding valleys of densities_Old Kiwi]] | ||
| + | * [[Lecture 28 - Final lecture_Old Kiwi]] | ||
==Course Topics== | ==Course Topics== | ||
| Line 51: | Line 65: | ||
* [[Learning algorithms_Old Kiwi]] (blank in old QE) | * [[Learning algorithms_Old Kiwi]] (blank in old QE) | ||
* [[Clustering_Old Kiwi]] | * [[Clustering_Old Kiwi]] | ||
| + | * [[Clustering Algorithms_Old Kiwi]] | ||
* [[Feature Extraction_Old Kiwi]] | * [[Feature Extraction_Old Kiwi]] | ||
* [[Estimation of Classifiability_Old Kiwi]] | * [[Estimation of Classifiability_Old Kiwi]] | ||
* [[Classifier evaluation_Old Kiwi]] (blank in old QE) | * [[Classifier evaluation_Old Kiwi]] (blank in old QE) | ||
* [[kNN Algorithm_Old Kiwi]] | * [[kNN Algorithm_Old Kiwi]] | ||
| − | + | * [[Editing technique_Old Kiwi]] | |
| − | + | ||
| − | + | ||
* [[Conjugate priors_Old Kiwi]] | * [[Conjugate priors_Old Kiwi]] | ||
| − | |||
* [[Artificial Neural Networks_Old Kiwi]] | * [[Artificial Neural Networks_Old Kiwi]] | ||
* [[Probabilistic neural networks_Old Kiwi]] | * [[Probabilistic neural networks_Old Kiwi]] | ||
| Line 65: | Line 77: | ||
* [[Mahalanobis Distance_Old Kiwi]] | * [[Mahalanobis Distance_Old Kiwi]] | ||
* [[ROC curves_Old Kiwi]] | * [[ROC curves_Old Kiwi]] | ||
| + | * [[Decision Tree_Old Kiwi]] | ||
| + | * [[Metrics and Similarity Measures_Old Kiwi]] | ||
| + | * [[K continuous derivatives_Old Kiwi]] | ||
| + | * [[Graph Algorithms_Old Kiwi]] | ||
| + | * [[Spectral Methods_Old Kiwi]] | ||
==Homework== | ==Homework== | ||
| − | * [[Homework 1_Old Kiwi]] | + | * [[Homework 1_Old Kiwi|HW1]] |
| − | * [[Homework 2_Old Kiwi]] | + | * [[Homework 2_Old Kiwi|HW2]] |
| − | * [[Homework Resources_Old Kiwi]] | + | * [[Homework 3_Old Kiwi|HW3]] |
| + | * [[Homework Resources_Old Kiwi|HW Resources]] | ||
| + | |||
| + | ==[[Applications of Pattern Recognition_Old Kiwi|Applications of Pattern Recognition]]== | ||
| − | + | This page can be used to discuss the applications of pattern recognition in our daily research! This would provide us an intuitive understanding of course topics. Please discuss "applied" pattern recognition here. Instead of just mentioning the field, please explain in detail how a specific tool of pattern recognition can be used in research. | |
| − | + | * [[Case-based Reasoning_Old Kiwi|Case-based Reasoning]] | |
| − | * [[ | + | * [[Wireless Communications_Old Kiwi|Wireless Communications]] |
| + | * [[Image Processing_Old Kiwi|Image Processing]] | ||
| + | * [[Implementation Issues_Old Kiwi|Implementation Issues]] | ||
| + | * [[Video Classification - State of the Art_Old Kiwi|Video Classification - State of the Art]] | ||
| − | ==[[Tools_Old Kiwi]]== | + | ==[[Tools_Old Kiwi|Tools]]== |
==[[ECE662:Glossary_Old Kiwi|Glossary]]== | ==[[ECE662:Glossary_Old Kiwi|Glossary]]== | ||
| − | ==[[Reference_Old Kiwi]]== | + | ==[[Reference_Old Kiwi|References]]== |
| − | * [[Pattern Recognition Journals_Old Kiwi]] | + | * [[Pattern Recognition Journals_Old Kiwi|Pattern Recognition Journals]] |
| − | * [[Pattern Recognition Conferences_Old Kiwi]] | + | * [[Pattern Recognition Conferences_Old Kiwi|Pattern Recognition Conferences]] |
| − | * [[Links to pattern recognition at other universities_Old Kiwi]] | + | * [[Links to pattern recognition at other universities_Old Kiwi|Links to pattern recognition at other universities]] |
| − | * [[Publications_Old Kiwi]] | + | * [[Publications_Old Kiwi|Publications]] |
==Textbooks== | ==Textbooks== | ||
| − | * [["Introduction to Statistical Pattern Recognition" by K. Fukunaga_Old Kiwi]] | + | * [["Introduction to Statistical Pattern Recognition" by K. Fukunaga_Old Kiwi|"Introduction to Statistical Pattern Recognition" by K. Fukunaga]] |
| − | * [["Pattern Classification" by Duda, Hart, and Stork_Old Kiwi]] | + | * [["Pattern Classification" by Duda, Hart, and Stork_Old Kiwi|"Pattern Classification" by Duda, Hart, and Stork]] |
| − | * [["Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler_Old Kiwi]] | + | * [["Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler_Old Kiwi|"Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler]] |
| − | * [["Pattern Recognition and Neural Networks" by Brian Ripley_Old Kiwi]] | + | * [["Pattern Recognition and Neural Networks" by Brian Ripley_Old Kiwi|"Pattern Recognition and Neural Networks" by Brian Ripley]] |
| − | * [["Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar_Old Kiwi]] | + | * [["Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar_Old Kiwi|"Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar]] |
| + | ---- | ||
| + | [[ECE662|Back to Main ECE 662 page]] | ||
Latest revision as of 05:18, 5 April 2013
Contents
ECE662, Spring 2008, Prof. Boutin
Introduction
This is the page for the Spring 2008 edition of the course ECE662: Pattern Recognition and Decision Making processes.
General Course Information
- Instructor: Mimi Boutin
- Office: MSEE342
- Email: mboutin at purdue dot edu
- Class meets Tu,Th 9-10:15am in ME118
- Office hours: Monday, Thursday 4-5pm
- TA hours: Thursday, 11:45 am-12:45 pm , EE 306
Course Website
Class Lecture Notes
- Lecture 1 - Introduction_Old Kiwi
- Lecture 2 - Decision Hypersurfaces_Old Kiwi
- Lecture 3 - Bayes classification_Old Kiwi
- Lecture 4 - Bayes Classification_Old Kiwi
- Lecture 5 - Discriminant Functions_Old Kiwi
- Lecture 6 - Discriminant Functions_Old Kiwi
- Lecture 7 - MLE and BPE_Old Kiwi
- Lecture 8 - MLE, BPE and Linear Discriminant Functions_Old Kiwi
- Lecture 9 - Linear Discriminant Functions_Old Kiwi
- Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_Old Kiwi
- Lecture 11 - Fischer's Linear Discriminant again_Old Kiwi
- Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi
- Lecture 13 - Kernel function for SVMs and ANNs introduction_Old Kiwi
- Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi
- Lecture 15 - Parzen Window Method_Old Kiwi
- Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi
- Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi
- Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi
- Lecture 19 - Nearest Neighbor Error Rates_Old Kiwi
- Lecture 20 - Density Estimation using Series Expansion and Decision Trees_Old Kiwi
- Lecture 21 - Decision Trees(Continued)_Old Kiwi
- Lecture 22 - Decision Trees and Clustering_Old Kiwi
- Lecture 23 - Spanning Trees_Old Kiwi
- Lecture 24 - Clustering and Hierarchical Clustering_Old Kiwi
- Lecture 25 - Clustering Algorithms_Old Kiwi
- Lecture 26 - Statistical Clustering Methods_Old Kiwi
- Lecture 27 - Clustering by finding valleys of densities_Old Kiwi
- Lecture 28 - Final lecture_Old Kiwi
Course Topics
- What is Pattern Recognition_Old Kiwi
- Bayesian Decision Theory_Old Kiwi
- Discriminant Function_Old Kiwi
- Parametric Estimators_Old Kiwi
- Nonparametric Estimators_Old Kiwi (blank in old QE)
- Learning algorithms_Old Kiwi (blank in old QE)
- Clustering_Old Kiwi
- Clustering Algorithms_Old Kiwi
- Feature Extraction_Old Kiwi
- Estimation of Classifiability_Old Kiwi
- Classifier evaluation_Old Kiwi (blank in old QE)
- kNN Algorithm_Old Kiwi
- Editing technique_Old Kiwi
- Conjugate priors_Old Kiwi
- Artificial Neural Networks_Old Kiwi
- Probabilistic neural networks_Old Kiwi
- Support Vector Machines_Old Kiwi
- Mahalanobis Distance_Old Kiwi
- ROC curves_Old Kiwi
- Decision Tree_Old Kiwi
- Metrics and Similarity Measures_Old Kiwi
- K continuous derivatives_Old Kiwi
- Graph Algorithms_Old Kiwi
- Spectral Methods_Old Kiwi
Homework
Applications of Pattern Recognition
This page can be used to discuss the applications of pattern recognition in our daily research! This would provide us an intuitive understanding of course topics. Please discuss "applied" pattern recognition here. Instead of just mentioning the field, please explain in detail how a specific tool of pattern recognition can be used in research.
- Case-based Reasoning
- Wireless Communications
- Image Processing
- Implementation Issues
- Video Classification - State of the Art
Tools
Glossary
References
- Pattern Recognition Journals
- Pattern Recognition Conferences
- Links to pattern recognition at other universities
- Publications
Textbooks
- "Introduction to Statistical Pattern Recognition" by K. Fukunaga
- "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
