- Schedule of Classes - June 18, 2018 7:14PM EDT
- Course Catalog - June 14, 2018 7:15PM EDT
Course information provided by the Courses of Study 2017-2018.
Course covering Bayesian inference for stochastic systems, parameter estimation and Bayesian networks. Includes optimal Bayesian filtering including Kalman filter, Hidden Markov model filter, sequential Monte-Carlo (particle) filters; maximum likelihood parameter estimation including the EM algorithm; social learning models and inference; Bayesian networks and their applications. The course will emphasize applications in social systems/networks, sensing and communication systems.
When Offered Spring.Outcomes
- Students will learn state of the art methods in Bayesian state estimation, parameter estimation and applications.
Regular Academic Session.
Credits and Grading Basis
3 Credits GradeNoAud(Letter grades only (no audit))
Class Number & Section Details
Taught in NYC. Enrollment limited to Cornell Tech students. *Weill students must obtain instructor approval to enroll. Please send completed registration forms and instructor approval to firstname.lastname@example.org. Add/drop dates: January 16th at 8 a.m. to February 7th at 4 p.m. Students will only be registered if space allows.
Disabled for this roster.