The course covers recent advances in the field of machine learning. Possible topics include: Learning Theory (PAC, error bounds, VC-dimension); Learning manifolds; Transfer learning; Active learning; Learning with structured data (e.g. graphs); Topic modeling; Learning with text; Graphical Models (Bayesian Networks); Learning HMMs. Topics may change depending on the instructor. Offered by Computer Science. May not be repeated for credit.
Deep Learn Generative Models
George Mason University
Daniel Barbara (firstname.lastname@example.org)
Times and Days
CS 681B-, 681XS, 687B-, 687XS, 688B- or 688XS.