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
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%
Introduction to &
ECE 455 / 555