(→Class Lecture Notes) |
|||
| (65 intermediate revisions by 29 users not shown) | |||
| Line 1: | Line 1: | ||
| + | [[Category:ECE662]] | ||
| + | |||
==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 15: | Line 18: | ||
[https://blackboard.purdue.edu/webct/logonDisplay.dowebct WebCT] | [https://blackboard.purdue.edu/webct/logonDisplay.dowebct WebCT] | ||
| + | |||
| + | [http://balthier.ecn.purdue.edu/index.php/ECE662:ChangeLog Changelog] | ||
==Class Lecture Notes== | ==Class Lecture Notes== | ||
| − | * [[Lecture 1 - Introduction_OldKiwi]] | + | * [[Lecture 1 - Introduction_OldKiwi|Lecture 1 - Introduction]] |
| − | * [[Lecture 2 - Decision Hypersurfaces_OldKiwi]] | + | * [[Lecture 2 - Decision Hypersurfaces_OldKiwi|Lecture 2 - Decision Hypersurfaces]] |
| − | * [[Lecture 3 - Bayes classification_OldKiwi]] | + | * [[Lecture 3 - Bayes classification_OldKiwi|Lecture 3 - Bayes classification]] |
| − | * [[Lecture 4 - Bayes Classification_OldKiwi]] | + | * [[Lecture 4 - Bayes Classification_OldKiwi|Lecture 4 - Bayes Classification]] |
| − | * [[Lecture 5 - Discriminant Functions_OldKiwi]] | + | * [[Lecture 5 - Discriminant Functions_OldKiwi|Lecture 5 - Discriminant Functions]] |
| − | * [[Lecture 6 - Discriminant Functions_OldKiwi]] | + | * [[Lecture 6 - Discriminant Functions_OldKiwi|Lecture 6 - Discriminant Functions]] |
| − | * [[Lecture 7 - MLE and BPE_OldKiwi]] | + | * [[Lecture 7 - MLE and BPE_OldKiwi|Lecture 7 - MLE and BPE]] |
| − | * [[Lecture 8 - MLE, BPE and Linear Discriminant Functions_OldKiwi]] | + | * [[Lecture 8 - MLE, BPE and Linear Discriminant Functions_OldKiwi|Lecture 8 - MLE, BPE and Linear Discriminant Functions]] |
| − | * [[Lecture 9 - Linear Discriminant Functions_OldKiwi]] | + | * [[Lecture 9 - Linear Discriminant Functions_OldKiwi|Lecture 9 - Linear Discriminant Functions]] |
| − | * [[Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_OldKiwi]] | + | * [[Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_OldKiwi|Lecture 10 - Batch Perceptron and Fisher Linear Discriminant]] |
| − | * [[Lecture 11 - Fischer's Linear Discriminant again_OldKiwi]] | + | * [[Lecture 11 - Fischer's Linear Discriminant again_OldKiwi|Lecture 11 - Fischer's Linear Discriminant again]] |
| − | * [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi]] | + | * [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|Lecture 12 - Support Vector Machine and Quadratic Optimization Problem]] |
| − | * [[Lecture 13 - Kernel function for SVMs and ANNs introduction_OldKiwi]] | + | * [[Lecture 13 - Kernel function for SVMs and ANNs introduction_OldKiwi|Lecture 13 - Kernel function for SVMs and ANNs introduction]] |
| − | * [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi]] | + | * [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)]] |
| − | * [[Lecture 15 - Parzen Window Method_OldKiwi]] | + | * [[Lecture 15 - Parzen Window Method_OldKiwi|Lecture 15 - Parzen Window Method]] |
| − | * [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi]] | + | * [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate]] |
| − | * [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi]] | + | * [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|Lecture 17 - Nearest Neighbors Clarification Rule and Metrics]] |
| − | * [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi]] | + | * [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)]] |
| + | * [[Lecture 19 - Nearest Neighbor Error Rates_OldKiwi|Lecture 19 - Nearest Neighbor Error Rates]] | ||
| + | * [[Lecture 20 - Density Estimation using Series Expansion and Decision Trees_OldKiwi|Lecture 20 - Density Estimation using Series Expansion and Decision Trees]] | ||
| + | * [[Lecture 21 - Decision Trees(Continued)_OldKiwi|Lecture 21 - Decision Trees(Continued)]] | ||
| + | * [[Lecture 22 - Decision Trees and Clustering_OldKiwi|Lecture 22 - Decision Trees and Clustering]] | ||
| + | * [[Lecture 23 - Spanning Trees_OldKiwi|Lecture 23 - Spanning Trees]] | ||
| + | * [[Lecture 24 - Clustering and Hierarchical Clustering_OldKiwi|Lecture 24 - Clustering and Hierarchical Clustering]] | ||
| + | * [[Lecture 25 - Clustering Algorithms_OldKiwi|Lecture 25 - Clustering Algorithms]] | ||
| + | * [[Lecture 26 - Statistical Clustering Methods_OldKiwi|Lecture 26 - Statistical Clustering Methods]] | ||
| + | * [[Lecture 27 - Clustering by finding valleys of densities_OldKiwi|Lecture 27 - Clustering by finding valleys of densities]] | ||
| + | * [[Lecture 28 - Final lecture_OldKiwi|Lecture 28 - Final lecture]] | ||
==Course Topics== | ==Course Topics== | ||
| − | * [[What is Pattern Recognition_OldKiwi]] | + | * [[What is Pattern Recognition_OldKiwi|What is Pattern Recognition]] |
| − | * [[Bayesian Decision Theory_OldKiwi]] | + | * [[Bayesian Decision Theory_OldKiwi|Bayesian Decision Theory]] |
| − | * [[Discriminant Function_OldKiwi]] | + | * [[Discriminant Function_OldKiwi|Discriminant Function]] |
| − | * [[Parametric Estimators_OldKiwi]] | + | * [[Parametric Estimators_OldKiwi|Parametric Estimators]] |
| − | * [[Nonparametric Estimators_OldKiwi]] (blank in old QE) | + | * [[Nonparametric Estimators_OldKiwi|Nonparametric Estimators]] (blank in old QE) |
| − | * [[Learning algorithms_OldKiwi]] (blank in old QE) | + | * [[Learning algorithms_OldKiwi|Learning algorithms]] (blank in old QE) |
| − | * [[Clustering_OldKiwi]] | + | * [[Clustering_OldKiwi|Clustering]] |
| − | * [[Feature Extraction_OldKiwi]] | + | * [[Clustering Algorithms_OldKiwi|Clustering Algorithms]] |
| − | * [[Estimation of Classifiability_OldKiwi]] | + | * [[Feature Extraction_OldKiwi|Feature Extraction]] |
| − | * [[Classifier evaluation_OldKiwi]] (blank in old QE) | + | * [[Estimation of Classifiability_OldKiwi|Estimation of Classifiability]] |
| − | + | * [[Classifier evaluation_OldKiwi|Classifier evaluation]] (blank in old QE) | |
| − | + | * [[kNN Algorithm_OldKiwi|kNN Algorithm]] | |
| − | + | * [[Editing technique_OldKiwi|Editing technique]] | |
| − | * [[ | + | * [[Conjugate priors_OldKiwi|Conjugate priors]] |
| − | * [[ | + | * [[Artificial Neural Networks_OldKiwi|Artificial Neural Networks]] |
| − | * [[Artificial Neural Networks_OldKiwi]] | + | * [[Probabilistic neural networks_OldKiwi|Probabilistic neural networks]] |
| − | * [[Probabilistic neural networks_OldKiwi]] | + | * [[Support Vector Machines_OldKiwi|Support Vector Machines]] |
| − | * [[Support Vector Machines_OldKiwi]] | + | * [[Mahalanobis Distance_OldKiwi|Mahalanobis Distance]] |
| − | * [[Mahalanobis Distance_OldKiwi]] | + | * [[ROC curves_OldKiwi|ROC curves]] |
| − | * [[ROC curves_OldKiwi]] | + | * [[Decision Tree_OldKiwi|Decision Tree]] |
| + | * [[Metrics and Similarity Measures_OldKiwi|Metrics and Similarity Measures]] | ||
| + | * [[K continuous derivatives_OldKiwi|K continuous derivatives]] | ||
| + | * [[Graph Algorithms_OldKiwi|Graph Algorithms]] | ||
| + | * [[Spectral Methods_OldKiwi|Spectral Methods]] | ||
==Homework== | ==Homework== | ||
| − | * [[Homework 1_OldKiwi]] | + | * [[Homework 1_OldKiwi|Homework 1]] |
| − | * [[Homework 2_OldKiwi]] | + | * [[Homework 2_OldKiwi|Homework 2]] |
| − | * [[Homework Resources_OldKiwi]] | + | * [[Homework 3_OldKiwi|Homework 3]] |
| + | * [[Homework Resources_OldKiwi|Homework Resources]] | ||
| + | |||
| + | ==[[Forum_OldKiwi|Forum]]== | ||
| + | |||
| + | ==[[Applications of Pattern Recognition_OldKiwi|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_OldKiwi|Case-based Reasoning]] | |
| + | * [[Wireless Communications_OldKiwi|Wireless Communications]] | ||
| + | * [[Image Processing_OldKiwi|Image Processing]] | ||
| + | * [[Implementation Issues_OldKiwi|Implementation Issues]] | ||
| + | * [[Video Classification - State of the Art_OldKiwi|Video Classification - State of the Art]] | ||
| − | ==[[Tools_OldKiwi]]== | + | ==[[Tools_OldKiwi|Tools]]== |
==[[ECE662:Glossary_OldKiwi|Glossary]]== | ==[[ECE662:Glossary_OldKiwi|Glossary]]== | ||
| − | ==[[Reference_OldKiwi]]== | + | ==[[Reference_OldKiwi|References]]== |
| − | * [[Pattern Recognition Journals_OldKiwi]] | + | * [[Pattern Recognition Journals_OldKiwi|Pattern Recognition Journals]] |
| − | * [[Pattern Recognition Conferences_OldKiwi]] | + | * [[Pattern Recognition Conferences_OldKiwi|Pattern Recognition Conferences]] |
| − | * [[Links to pattern recognition at other universities_OldKiwi]] | + | * [[Links to pattern recognition at other universities_OldKiwi|Links to pattern recognition at other universities]] |
| − | * [[Publications_OldKiwi]] | + | * [[Publications_OldKiwi|Publications]] |
==Textbooks== | ==Textbooks== | ||
* [["Introduction to Statistical Pattern Recognition" by K. Fukunaga_OldKiwi]] | * [["Introduction to Statistical Pattern Recognition" by K. Fukunaga_OldKiwi]] | ||
| − | * [["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"]] |
| − | * [["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]] |
Latest revision as of 06:11, 13 February 2012
Contents
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
- Lecture 2 - Decision Hypersurfaces
- Lecture 3 - Bayes classification
- Lecture 4 - Bayes Classification
- Lecture 5 - Discriminant Functions
- Lecture 6 - Discriminant Functions
- Lecture 7 - MLE and BPE
- Lecture 8 - MLE, BPE and Linear Discriminant Functions
- Lecture 9 - Linear Discriminant Functions
- Lecture 10 - Batch Perceptron and Fisher Linear Discriminant
- Lecture 11 - Fischer's Linear Discriminant again
- Lecture 12 - Support Vector Machine and Quadratic Optimization Problem
- Lecture 13 - Kernel function for SVMs and ANNs introduction
- Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)
- Lecture 15 - Parzen Window Method
- Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate
- Lecture 17 - Nearest Neighbors Clarification Rule and Metrics
- Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)
- Lecture 19 - Nearest Neighbor Error Rates
- Lecture 20 - Density Estimation using Series Expansion and Decision Trees
- Lecture 21 - Decision Trees(Continued)
- Lecture 22 - Decision Trees and Clustering
- Lecture 23 - Spanning Trees
- Lecture 24 - Clustering and Hierarchical Clustering
- Lecture 25 - Clustering Algorithms
- Lecture 26 - Statistical Clustering Methods
- Lecture 27 - Clustering by finding valleys of densities
- Lecture 28 - Final lecture
Course Topics
- What is Pattern Recognition
- Bayesian Decision Theory
- Discriminant Function
- Parametric Estimators
- Nonparametric Estimators (blank in old QE)
- Learning algorithms (blank in old QE)
- Clustering
- Clustering Algorithms
- Feature Extraction
- Estimation of Classifiability
- Classifier evaluation (blank in old QE)
- kNN Algorithm
- Editing technique
- Conjugate priors
- Artificial Neural Networks
- Probabilistic neural networks
- Support Vector Machines
- Mahalanobis Distance
- ROC curves
- Decision Tree
- Metrics and Similarity Measures
- K continuous derivatives
- Graph Algorithms
- Spectral Methods
Homework
Forum
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_OldKiwi
- "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"
- ["Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar
