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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 E-mail:
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. Required Texts 1. Pattern Classification 2/e, Duda, Hart & Stork (classic text) 2. Pattern Recognition & Machine Learning—Bishop (comprehensive) Reference Materials 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% |
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ECE 555 Main | Lectures | Announcements | LABS | FAQ | Related Sites |
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Fall 2009 |
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Introduction to & Advanced Topics in Pattern Recognition |
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ECE 455 / 555 |
