Instructor    (the one...the only...the merciless…)

                                 Dr. Robi Polikar

Office: 136 Rowan Hall

Phone: 256 5372

Office Hours: Open door policy, as always

Class Meeting: Tuesdays & Thursdays —15:45-17:00

E-mail:

 

 

Syllabus with Tentative Schedule: Download  syllabus

This Fall in Pattern Recognition—PR’07 at a Glance

Bayesian classifiers, discriminant analysis, non-parametric density estimation, Parzen windows, K-nearest neighbor classifiers, probabilistic neural network, support vector machines, kernel methods, the perceptron model, multilayer perceptron and radial basis function neural networks, bias-variance dilemma, ensemble classifiers: bagging, boosting, AdaBoost, and Learn++, combination rules, incremental learning, feature selection, data fusion, confidence estimation, and more.

Texts and Reference Materials: (first two are required)

1. Pattern Recognition & Machine Learning—Bishop (comprehensive)

2. Introduction to Machine Learning—Alpaydin (introductory)

3. Combining Pattern Classifiers, Kuncheva (Awesome book…!)

4. Pattern Classification 2/e, Duda, Hart & Stork (classic text)

5. Statistical Pattern Recognition, Webb (Very good and readable)

6. Pattern Recognition: Theodoridis & Koutroumbras (comprehensive)

7. Dr. Gutierrez-Osuna’s PR Lectures (excellent site!!!)

Homework: Weekly reading and written homeworks will be assigned. Written homeworks will be graded, and you will be quizzed on reading assignments. Homeworks are im-por-tant! Late submissions are not accepted. There will also be a final project. The final project will constitute of an innovative application of pattern recognition on a topic of your interest. A formal project report will be submitted. Graduate students are expected to submit their project work to a (preferably IEEE) conference.

Exams: One or two midterms, unannounced quizzes.

Grading: HW 25% Midterm 25% Quizzes 10% Project 35% Prof. conduct 10%

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Fall  2007

Introduction to &
Advanced Topics in

Pattern Recognition

ECE 455 / 555