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* [[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 20 - Density Estimation using Series Expansion and Decision Trees_Old Kiwi]] | ||
| − | * [[Lecture 21 - Decision | + | * [[Lecture 21 - Decision Trees(Continued)_Old Kiwi]] |
==Course Topics== | ==Course Topics== | ||
Revision as of 11:59, 1 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
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 -- Missing images (ex. .. image:: NN_2layer_2.jpg)
- 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
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)
- kNN Algorithm_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 Trees_Old Kiwi
- Decision Tree_Old Kiwi
Homework
Forum_Old Kiwi
Applications of Pattern Recognition_Old Kiwi
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
- Wireless Communications_Old Kiwi
- Image Processing_Old Kiwi
- Implementation Issues_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
