EE 381K6 Estimation Theory [Spring 2024]
Lectures: MW 1:303:00pm, SZB 2.802
Office hours: MW 3:304:00pm, EER 7.810
Teaching Assistant: TBA
References:
S. M. Kay, Fundamentals of Statistical Signal Processing, Vol. I: Estimation Theory, PrenticeHall, 1993.
T. Kailath, A. Sayed, and B. Hassibi, Linear Estimation, PrenticeHall, 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 CramerRao bound; deterministic
and stochastic leastsquares; the innovation process; spectral factorization and Wiener filters; statespace
structure and Kalman filters; Markov chain Monte Carlo methods; Bayesian filtering and particle filters;
expectationmaximization algorithm; variational Bayesian methods with applications including autoencoders and
Bayesian neural networks; and nonparametric Bayesian models and methods.
Course outline (tentative):
 Introduction and Foundations
 parameter estimation: classical vs. Bayesian
 fundamental limits: the CramerRao lower bound
 from deterministic to stochastic leastsquares problems
 the innovation process
 Estimation of Stationary and Nonstationary Processes
 Wiener theory for scalar and vector processes
 Recursive Wiener filters
 statespace models and Kalman filter
 Bayesian filtering, MCMC methods, particle filters
 Variational Bayesian Methods with Applications
 expectationmaximization algorithm
 ELBO and mean field approximation
 autoencoders and Bayesian neural networks
 estimating uncertainty in learning
 Nonparametric 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, 4716259, http://www.utexas.edu/diversity/ddce/ssd/.
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 Exit in an orderly fashion and assemble outside.
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