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 —15:15-16:45
Syllabus with Tentative Schedule: Download syllabus
This Fall in Pattern Recognition
Bayesian classifiers, discriminant analysis, non-parametric density estimation, Parzen windows, K-nearest neighbor classifiers, probabilistic neural network, support vector machines, kernel methods, the multilayer perceptron and radial basis function neural networks, decision trees, unsupervised clustering, ensemble classifiers, boosting, AdaBoost, and Learn++, incremental learning, feature selection, data fusion, confidence estimation, nonstationary learning and more.
1. Pattern Classification 2/e, Duda, Hart & Stork (classic text)
2. Pattern Recognition & Machine Learning—Bishop (comprehensive)
1. Introduction to Machine Learning—Alpaydin (very introductory)
2. Combining Pattern Classifiers, Kuncheva (Very readable book…!)
3. Neural Networks and Learning Machines 3/e, Haykin (comprehensive)
4. Pattern Recognition 4/e: Theodoridis & Koutroumbras (comprehensive)
5. Learning from Data—Cherkassky, Wiley, 2007.
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: Two midterms, quizzes, oral reviews.
Grading: HW 25% Midterm 25% Quizzes 10% Project 30% Prof. conduct 10%
Introduction to & Advanced Topics
ECE 455 / 555