| Line 69: | Line 69: | ||
* [[Metrics and Similarity Measures_OldKiwi]] | * [[Metrics and Similarity Measures_OldKiwi]] | ||
* [[K continuous derivatives_OldKiwi]] | * [[K continuous derivatives_OldKiwi]] | ||
| + | * [[Clustering_OldKiwi]] | ||
[[Special:Lonelypages_OldKiwi|'''Lots, lots more''']] | [[Special:Lonelypages_OldKiwi|'''Lots, lots more''']] | ||
Revision as of 15:52, 8 April 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
- TA hours: Thursday, 11:45 am-12:45 pm , EE 306
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
- 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
- Lecture 19 - Nearest Neighbor Error Rates_OldKiwi
- Lecture 20 - Density Estimation using Series Expansion and Decision Trees_OldKiwi
- Lecture 21 - Decision Trees(Continued)_OldKiwi
- Lecture 22 - Decision Trees and Clustering_OldKiwi
- Lecture 23 - Spanning Trees_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)
- kNN Algorithm_OldKiwi
- Conjugate priors_OldKiwi
- Artificial Neural Networks_OldKiwi
- Probabilistic neural networks_OldKiwi
- Support Vector Machines_OldKiwi
- Mahalanobis Distance_OldKiwi
- ROC curves_OldKiwi
- Decision Tree_OldKiwi
- Metrics and Similarity Measures_OldKiwi
- K continuous derivatives_OldKiwi
- Clustering_OldKiwi
Homework
Forum_OldKiwi
Applications of Pattern Recognition_OldKiwi
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
- Wireless Communications_OldKiwi
- Image Processing_OldKiwi
- Implementation Issues_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
