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==Introduction== | ==Introduction== | ||
| − | This is the page for the Spring 2008 edition of the course ECE662: Pattern Recognition and Decision Making processes. | + | This is the page for the Spring 2008 edition of the course [[ECE662|ECE662: Pattern Recognition and Decision Making processes]]. |
===General Course Information=== | ===General Course Information=== | ||
| − | * Instructor: Mimi Boutin | + | * Instructor: [[user:mboutin|Mimi Boutin]] |
* Office: MSEE342 | * Office: MSEE342 | ||
* Email: mboutin at purdue dot edu | * Email: mboutin at purdue dot edu | ||
Latest revision as of 07:11, 13 February 2012
Contents
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
Class Lecture Notes
- Lecture 1 - Introduction
- Lecture 2 - Decision Hypersurfaces
- Lecture 3 - Bayes classification
- Lecture 4 - Bayes Classification
- Lecture 5 - Discriminant Functions
- Lecture 6 - Discriminant Functions
- Lecture 7 - MLE and BPE
- Lecture 8 - MLE, BPE and Linear Discriminant Functions
- Lecture 9 - Linear Discriminant Functions
- Lecture 10 - Batch Perceptron and Fisher Linear Discriminant
- Lecture 11 - Fischer's Linear Discriminant again
- Lecture 12 - Support Vector Machine and Quadratic Optimization Problem
- Lecture 13 - Kernel function for SVMs and ANNs introduction
- Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)
- Lecture 15 - Parzen Window Method
- Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate
- Lecture 17 - Nearest Neighbors Clarification Rule and Metrics
- Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)
- Lecture 19 - Nearest Neighbor Error Rates
- Lecture 20 - Density Estimation using Series Expansion and Decision Trees
- Lecture 21 - Decision Trees(Continued)
- Lecture 22 - Decision Trees and Clustering
- Lecture 23 - Spanning Trees
- Lecture 24 - Clustering and Hierarchical Clustering
- Lecture 25 - Clustering Algorithms
- Lecture 26 - Statistical Clustering Methods
- Lecture 27 - Clustering by finding valleys of densities
- Lecture 28 - Final lecture
Course Topics
- What is Pattern Recognition
- Bayesian Decision Theory
- Discriminant Function
- Parametric Estimators
- Nonparametric Estimators (blank in old QE)
- Learning algorithms (blank in old QE)
- Clustering
- Clustering Algorithms
- Feature Extraction
- Estimation of Classifiability
- Classifier evaluation (blank in old QE)
- kNN Algorithm
- Editing technique
- Conjugate priors
- Artificial Neural Networks
- Probabilistic neural networks
- Support Vector Machines
- Mahalanobis Distance
- ROC curves
- Decision Tree
- Metrics and Similarity Measures
- K continuous derivatives
- Graph Algorithms
- Spectral Methods
Homework
Forum
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_OldKiwi
- "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"
- ["Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar
