ECE 5412

ECE 5412

Course information provided by the Courses of Study 2018-2019.

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.

  • Students will learn state of the art methods in Bayesian state estimation, parameter estimation and applications.

View Enrollment Information

  •   Regular Academic Session. 

  • 3 Credits GradeNoAud

  • 12378 ECE 5412   LEC 030

  • Taught in NYC. Enrollment limited to Cornell Tech students. Cornell Tech students must have taken ECE 5411 as a pre requisite for the course. If they have not, they should contact the professor for a placement exam. *Weill students must obtain instructor approval to enroll. Please send completed registration forms and instructor approval to Add/drop dates: January 16th at 8 a.m. to February 7th at 4 p.m. Students will only be registered if space allows.