ECE 5412

ECE 5412

Course information provided by the Courses of Study 2022-2023.

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 discussion sections will focus on more advanced aspects in statistical inference.

When Offered Spring.

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

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Syllabi:
  •   Regular Academic Session.  Choose one lecture and one discussion.

  • 4 Credits GradeNoAud

  •  9974 ECE 5412   LEC 001

    • TR Phillips Hall 307
    • Jan 23 - May 9, 2023
    • Krishnamurthy, V

  • Instruction Mode: In Person

  • 10282 ECE 5412   DIS 201

    • F Phillips Hall 307
    • Jan 23 - May 9, 2023
    • Krishnamurthy, V

  • Instruction Mode: In Person