Latest revision as of 05:18, 5 April 2013

ECE662, Spring 2008, Prof. Boutin

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

Course Webpage

Current Kiwi

WebCT

Changelog

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
• 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
• Lecture 22 - Decision Trees and Clustering_Old Kiwi
• Lecture 23 - Spanning Trees_Old Kiwi
• Lecture 24 - Clustering and Hierarchical Clustering_Old Kiwi
• Lecture 25 - Clustering Algorithms_Old Kiwi
• Lecture 26 - Statistical Clustering Methods_Old Kiwi
• Lecture 27 - Clustering by finding valleys of densities_Old Kiwi
• Lecture 28 - Final lecture_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
• Clustering Algorithms_Old Kiwi
• Feature Extraction_Old Kiwi
• Estimation of Classifiability_Old Kiwi
• Classifier evaluation_Old Kiwi (blank in old QE)
• kNN Algorithm_Old Kiwi
• Editing technique_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 Tree_Old Kiwi
• Metrics and Similarity Measures_Old Kiwi
• K continuous derivatives_Old Kiwi
• Graph Algorithms_Old Kiwi
• Spectral Methods_Old Kiwi

Homework

• HW1
• HW2
• HW3
• HW Resources

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
• "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" by Brian Ripley
• "Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar

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