Instructor: Dr. Daniel Barbará
Description: This course covers statistical pattern recognition and learning theory. Topics include Bayesian classification and decision theory, density estimation, discriminant analysis, Maximum Likelihood estimation, Bayesian estimation, dimensionality reduction, support vector machines, and learning theory (PAC, error bounds, VC-dimension).
Meeting Times and Locations:
Textbook: R. Duda, P. Hart and D. Stork, Pattern Classification, Wiley, 2002.
C.M. Bishop. Pattern Recognition and Machine Learning, Springer, 2006.
Office Hours: By appointment(Office: ST II, Room 353)
Course Web Page: http://cs.gmu.edu/~dbarbara/CS775/index.html
No early exams will be given and make-up exams are strongly
GMU Honor Code will be enforced. The students are supposed to work individually on the assignments/projects. We reserve the right to use MOSS to detect plagiarism. Violations of GMU Honor Code or a total score of 49 (or less) will result in an F.
Computer Accounts: All students should have accounts on the central Mason Unix system mason.gmu.edu (also known as osf1.gmu.edu) and on IT&E Unix cluster zeus.ite.gmu.edu (Instructions and related links are here). Students can work in IT&E computer labs for programming projects during the specified hours.
Students with Disabilities: If you have a documented learning disability or other condition that may affect academic performance you should: 1) make sure this documentation is on file with the Office of Disability Services (SUB I, Rm. 222; 993-2474; www.gmu.edu/student/drc) to determine the accommodations you need; and 2) talk with me to discuss your accommodation needs.