Prerequisites: CS 580
Instructor: Prof. Harry Wechsler wechsler@gmu.edu
Course Description – Course explores statistical pattern recognition, statistical learning theory, and neural networks. Topics include model selection and prediction, Bayesian decision theory and classifiers, (parametric and nonparametric) density estimation, linear and nonlinear discriminant analysis, mixture models and expectation – maximization (EM), feature selection and dimensionality reduction, , semi-supervised learning and collective classification, and vector quantization, self-organization maps and (spectral) clustering. Course emphasizes experimental design and empirical evaluation, applications, uncontrolled settings (incomplete information and uncertainty), learning from labeled and unlabeled data, and performance evaluation.
Time, Day, and Venue: R – Thursday, 4:30 – 7:10 pm,
Innovation Hall 134
Office Hours: R – Thursday, 3:00 – 4:00 pm, ENGR 4448
http://registrar.gmu.edu/calendars/fall-2014/
First day of classes: Thursday, August 28
Thanksgiving recess: no class on Thursday, November 27
Last day of classes: Thursday, December 4
http://registrar.gmu.edu/calendars/fall-2014/exams/
Final Exam: Thursday, December 11, 4:30 – 7:15 pm
Textbook: (required) Theodoridis and Koutroumbas, Pattern Recognition (4th ed.), Elsevier / Academic Press, 2008 and (supplementary) Theodoridis, Pikrakis, Koutroumbas, and Cavouras, Introduction to Pattern Recognition – A MATLAB Approach --- Elsevier / Academic Press, 2010.
· Homework – 20%
· Mid Term – Thursday, October 16 – 20 %
· Term Project – December 4 – 20 %
· Final – Thursday, December 11 - 40 %
http://www.fcps.edu/southcountyhs/sservices/gradescale.html
You are expected to abide by the GMU honor code. Homework assignments and exams are individual efforts. Information on the university honor code can be found at
http://oai.gmu.edu/the-mason-honor-code/
Additional departmental CS information: http://cs.gmu.edu/wiki/pmwiki.php/HonorCode/CSHonorCodePolicies