The aim of this course is to introduce the fundamentals of estimation theory to graduate students. In particular, the course will focus on the applications of estimation theory to signal processing. The first part of the course will cover the concept of minimum variance unbiased estimation, Cramer-Rao lower bound, best linear unbiased estimators, maximum likelihood estimation, and least square estimation. The second part of the course will focus on general and linear Bayesian estimation and Kalman filters. The course expects maturity in 1) the basics of probability and random process, 2) linear and matrix algebra. Weekly hours: 3 Lecture hoursPrerequisite(s): Instructor permission is required.
The aim of this course is to introduce the fundamentals of estimation theory to graduate students. In particular, the course will focus on the applications of estimation theory to signal processing. The first part of the course will cover the concept of minimum variance unbiased estimation, Cramer-Rao lower bound, best linear unbiased estimators, maximum likelihood estimation, and least square estimation. The second part of the course will focus on general and linear Bayesian estimation and Kalman filters. The course expects maturity in 1) the basics of probability and random process, 2) linear and matrix algebra. Weekly hours: 3 Lecture hoursPrerequisite(s): Instructor permission is required.
