- Schedule of Classes - January 8, 2020 7:14PM EST
- Course Catalog - January 8, 2020 7:15PM EST
Course information provided by the Courses of Study 2019-2020.
Covers essential topics in high dimensional statistical inference, stochastic optimization, Bayesian statistical signal processing and Markov Chain Monte-Carlo stochastic simulation. The course is four inter-related parts. Part 1 covers the basics of probabilistic models, Markov chain Monte-Carlo simulation and regression with sparsity constraints. Part 2 covers Bayesian filtering including the Kalman filter, Hidden Markov Model filter and sequential Markov chain Monte-Carlo methods such as the particle filter. Part 3 covers maximum likelihood estimation and numerical methods such as the Expectation Maximization algorithm. Part 4 covers stochastic gradient algorithms and stochastic optimization. The course focuses on the deep fundamental ideas that underpin signal processing, data science and machine learning - the assignments and project will explore applications.
When Offered Spring.Outcomes
- Students will learn state of the art methods in Bayesian state estimation, parameter estimation and applications.
Disabled for this roster.