ECE 5412

ECE 5412

Course information provided by the Courses of Study 2020-2021.

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 Fall.

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

View Enrollment Information

Enrollment Information
Syllabi: 1 available
  •   Regular Academic Session. 

  • 3 Credits GradeNoAud

  • 11630ECE 5412  LEC 001

    • TROnline Meeting
    • Sep 2 - Dec 16, 2020
    • Krishnamurthy, V

  • Instruction Mode: Online