ECE 532: Theory and Applications of Pattern Recognition

Prerequisites: 

Competence in basic probability and statistics, 
e.g., ECE 331 or Math 431
Instructor: 

        Robert Nowak
        E-mail: nowak@engr.wisc.edu
        Web: http://nowak.ece.wisc.edu
        Phone:  608 265 3914 
        3627 Engineering Hall
        Office Hours: after lecture, Tuesday and Thursday, 2:15-3:15pm

Lectures: 

        Spring 2005
        Tuesday, Thursday 1:00 - 2:15 PM 
        2535 Engineering Hall

Textbook: Pattern Classification, by R. O. Duda, P. E. Hart and D. G. Stork, Second Edition, Wiley.
			Textbook webpage with additional information
Grading and Evaluation: 

Midterm Exam: March 17, 25% of total course grade
Course Project: 30% (to be handed in on April 30)
Final Exam: May 8, 30% of total course grade
Homework & Course Participation: 15%

Project Teams and Web Reports: 

Team 1 John Boehm and Minglei Huang
Team 2 Aarti Singh and Raman Arora
Team 3 Tulaya Limpiti and David Winters
Team 4 William L'Huilliler, Aline Martin and Ercan Yildiz
Team 5 Sean Gonzalez and Kin-chung Wong
Team 6 Laurence Choi and Roengrut Rujanakraikarn (Tae)

Homework Problems: 

Homework 1 (pdf)
Homework 2 (pdf)
Homework 3 (pdf)
Homework 4 (pdf)
Homework 5 (pdf)

Homework 6 (pdf)
iris.mat (right-click to save)
Homework 7 (pdf)

Homework 8 (pdf)
Project Task 1 (pdf)

Homework 9 (pdf)

Project - Final Goals and Objectives (pdf)
Homework 10 (pdf)

Keeping up with the course and participating in lectures (asking questions)
is very important to successful learning. Keep your homework solutions organized
in a folder or binder. I will ask you to turn in your solutions from time to time
to see how you are keeping up with the coursework.

Course Outline:

1. Pattern Recognition Systems
- data collection
- feature selection
- classifiers
- classifier design and training
- supervised and unsupervised learning

2. Basic Decision Theory
- Bayesian decision theory
- Minimum error-rate classification
- Classifiers and decision boundaries

3. Parametric Methods
- Multivariate Gaussian model
- Class-conditional densities
- Sufficient statistics and model fitting
- Expectation-Maximization algorithm
- Overfitting and dimensionality reduction

4. Nonparametric Methods
- Density estimation
- Histogram classification rule
- Decision trees
- Nearest-neighbor classification
- Kernel methods

5. Statistical Learning Theory
- Complexity and regularization
- Probably Approximately Correct learning
- Chernoff's bound
- Distribution-free error bounds for classification

6. Statistical Analysis of Classifiers
        - Analysis of histogram rule
        - Analysis of decision trees
        - Vapnik-Chevronenkis inequality
        - Analysis of linear classifiers


Reference Materials:
George Phillip's Lecture on Crystallography (ppt)
George Phillip's Lab
Crystallography Datasets:
Crystal Dataset A
Crystal Dataset B
Demos:
demo1.m