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* [[Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_OldKiwi]] | * [[Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_OldKiwi]] | ||
* [[Lecture 11 - Fischer's Linear Discriminant again_OldKiwi]] | * [[Lecture 11 - Fischer's Linear Discriminant again_OldKiwi]] | ||
| − | * [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi]] | + | * [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi]] -- still needs changes |
* [[Lecture 13 - Kernel function for SVMs and ANNs introduction_OldKiwi]] | * [[Lecture 13 - Kernel function for SVMs and ANNs introduction_OldKiwi]] | ||
* [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi]] | * [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi]] | ||
Revision as of 08:34, 20 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_OldKiwi
- Lecture 2 - Decision Hypersurfaces_OldKiwi
- Lecture 3 - Bayes classification_OldKiwi
- Lecture 4 - Bayes Classification_OldKiwi
- Lecture 5 - Discriminant Functions_OldKiwi
- Lecture 6 - Discriminant Functions_OldKiwi
- Lecture 7 - MLE and BPE_OldKiwi
- Lecture 8 - MLE, BPE and Linear Discriminant Functions_OldKiwi
- Lecture 9 - Linear Discriminant Functions_OldKiwi
- Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_OldKiwi
- Lecture 11 - Fischer's Linear Discriminant again_OldKiwi
- Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi -- still needs changes
- Lecture 13 - Kernel function for SVMs and ANNs introduction_OldKiwi
- Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi
- Lecture 15 - Parzen Window Method_OldKiwi
- Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi
- Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi
- Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi
Course Topics
- What is Pattern Recognition_OldKiwi
- Bayesian Decision Theory_OldKiwi
- Discriminant Function_OldKiwi
- Parametric Estimators_OldKiwi
- Nonparametric Estimators_OldKiwi (blank in old QE)
- Learning algorithms_OldKiwi (blank in old QE)
- Clustering_OldKiwi
- Feature Extraction_OldKiwi
- Estimation of Classifiability_OldKiwi
- Classifier evaluation_OldKiwi (blank in old QE)
Topics that don't have any links to them...
(Well, at least the didn't before...)
- Conjugate priors_OldKiwi
- Genetic algorithms_OldKiwi
- Artificial Neural Networks_OldKiwi
- Probabilistic neural networks_OldKiwi
- Support Vector Machines_OldKiwi
- Mahalanobis Distance_OldKiwi
- ROC curves_OldKiwi
Homework
Forum_OldKiwi
Tools_OldKiwi
Glossary
Reference_OldKiwi
- Pattern Recognition Journals_OldKiwi
- Pattern Recognition Conferences_OldKiwi
- Links to pattern recognition at other universities_OldKiwi
- Publications_OldKiwi
Textbooks
- "Introduction to Statistical Pattern Recognition" by K. Fukunaga_OldKiwi
- "Pattern Classification" by Duda, Hart, and Stork_OldKiwi
- "Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler_OldKiwi
- "Pattern Recognition and Neural Networks" by Brian Ripley_OldKiwi
- "Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar_OldKiwi
