DEPARTMENT OF COMPUTER SCIENCE
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
Grading:
No early exams will be given and make-up exams are strongly
discouraged.
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.