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Lecture Notes |
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Syllabus and instructional objectives are also available for download. Make sure that you check the last updated notice at the bottom, since you will be responsible from the latest version at all times.
Date Topic Pre-class Lecture 0 / Math Fundamentals (Also see announcements page) September 1, 3* Introduction / Bayes Theory September 8, 10 Bayes Decision Theory September 15, 17 Density Estimation September 22, 24 PCA/FLD / Sept/Oct 29, 1 Linear Classifiers October 6, 8 Multilayer Perceptrons - EXAM I October 13, 15 RBF networks / Exam review October 20, 22 Support Vector Machines (SVM) October 27, 29 SVMs (cont.)/ Structural R.M. November 3, 5 Ensemble Learning / Boosting November 10, 12 Incremental / Nonstationary Learning November 17, 19 Mixture Models / EM Alg. / RVM November 24*, 26* Unsupervised Learning / Clustering December 1, 3 Decision Trees / CART, ID3, C4.5 December 8,10 Hidden Markov Models December 15,17 Project Presentations
Bold: Midterm week
(*Dr. Polikar may be away—Class will be delivered via Skype or |
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Introduction to & Advanced Topics in Pattern Recognition ECE 455 /555 |