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* [[Lecture 8 - MLE, BPE and Linear Discriminant Functions_Old Kiwi]] | * [[Lecture 8 - MLE, BPE and Linear Discriminant Functions_Old Kiwi]] | ||
* [[Lecture 9 - Linear Discriminant Functions_Old Kiwi]] | * [[Lecture 9 - Linear Discriminant Functions_Old Kiwi]] | ||
| − | * [[Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_Old Kiwi]] | + | * [[Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_Old Kiwi]] |
* [[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]] | ||
Revision as of 18:38, 22 March 2008
Contents
Introduction
This is the page for 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
Course Website
Class Lecture Notes
- Lecture 1 - Introduction_Old Kiwi
- Lecture 2 - Decision Hypersurfaces_Old Kiwi -- Missing figures
- Lecture 3 - Bayes classification_Old Kiwi
- Lecture 4 - Bayes Classification_Old Kiwi
- Lecture 5 - Discriminant Functions_Old Kiwi -- Missing Figures
- 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 -- Nearly empty
- Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi
- Lecture 15 - Parzen Window Method_Old Kiwi -- nearly empty
- Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi
- Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi -- Missing some figures
- Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_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
- Feature Extraction_Old Kiwi
- Estimation of Classifiability_Old Kiwi
- Classifier evaluation_Old Kiwi (blank in old QE)
Topics that don't have any links to them...
(Well, at least the didn't before...)
- Conjugate priors_Old Kiwi
- Genetic algorithms_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
Homework
Forum_Old Kiwi
Tools_Old Kiwi
Glossary
Reference_Old Kiwi
- Pattern Recognition Journals_Old Kiwi
- Pattern Recognition Conferences_Old Kiwi
- Links to pattern recognition at other universities_Old Kiwi
- Publications_Old Kiwi
Textbooks
- "Introduction to Statistical Pattern Recognition" by K. Fukunaga_Old Kiwi
- "Pattern Classification" by Duda, Hart, and Stork_Old Kiwi
- "Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler_Old Kiwi
- "Pattern Recognition and Neural Networks" by Brian Ripley_Old Kiwi
- "Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar_Old Kiwi
