EE 381K-6 Estimation Theory [Spring 2024]
Lectures: MW 1:30-3:00pm, SZB 2.802
Office hours: MW 3:30-4:00pm, EER 7.810
Teaching Assistant: TBA
References:
S. M. Kay, Fundamentals of Statistical Signal Processing, Vol. I: Estimation Theory, Prentice-Hall, 1993.
T. Kailath, A. Sayed, and B. Hassibi, Linear Estimation, Prentice-Hall, 2000.
K. P. Murphy, Probabilistic Machine Learning: Advanced Topics, MIT Press, 2023.
Grading (tentative)
Homework policy: Homeworks are to be submitted at the
beginning of the class when they are due. You may discuss homework problems
with other students, but must submit your own independent solution. Late
homework assignments will not be accepted.
Prerequisites: EE381J Probability and Stochastic Processes I (or equivalent) and background in
linear algebra (e.g., at the level of EE381K Convex Optimization) are required.
Course description:
Introduction to the fundamentals of estimation theory, with applications to stochastic signal processing and
machine learning. Topics include classical and Bayesian parameter estimation; the Cramer-Rao bound; deterministic
and stochastic least-squares; the innovation process; spectral factorization and Wiener filters; state-space
structure and Kalman filters; Markov chain Monte Carlo methods; Bayesian filtering and particle filters;
expectation-maximization algorithm; variational Bayesian methods with applications including autoencoders and
Bayesian neural networks; and non-parametric Bayesian models and methods.
Course outline (tentative):
- Introduction and Foundations
- parameter estimation: classical vs. Bayesian
- fundamental limits: the Cramer-Rao lower bound
- from deterministic to stochastic least-squares problems
- the innovation process
- Estimation of Stationary and Non-stationary Processes
- Wiener theory for scalar and vector processes
- Recursive Wiener filters
- state-space models and Kalman filter
- Bayesian filtering, MCMC methods, particle filters
- Variational Bayesian Methods with Applications
- expectation-maximization algorithm
- ELBO and mean field approximation
- autoencoders and Bayesian neural networks
- estimating uncertainty in learning
- Non-parametric Bayesian Models and Methods
Notice for students with disabilities
Students with disabilities may request appropriate academic accommodations from the Division of Diversity and Community Engagement, Services for Students with Disabilities, 471-6259, http://www.utexas.edu/diversity/ddce/ssd/.
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