ECE 6540 - Estimation Theory

Electrical and Computer Engineering Department

Course Info

Lectures: WF 11:50 AM - 1:10 PM in WEB 1460

Instructor: Tolga Tasdizen
Email: tolga@sci.utah.edu
Office: WEB 3887
Office Hours: Wednesdays 10:30-11:30 AM. Also with appoointment outside regular office hours.

Grader: Harsha Rao
Email: hrao@eng.utah.edu
Office: MEB 2430
Office Hours (for questions about HW grading): Thursdays 5-6 pm or setup an appointment with email

Required Textbook: Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory
                                 Steven M. Kay
                                 Prentice Hall, Upper Saddle River, NJ 07458
                                 ISBN: 0-13-345711-7

Prerequisite: ECE 5510 and 5530

Detailed course information and syllabus

Important Notices

(Posted 8/24) Read Chapters 1 & 2 from textbook
(Posted 9/8) Read Chapter 3 Sections 1 through 6 & Appendix 3A
(Posted 9/14) Read Chapter 3 Sections 7 through 9 & Appendix 3B. Optional reading: Sections 10 and 11, Appendices 3C and 3D
(Posted 9/16) Read Chapter 4 Sections 1 through 4
(Posted 9/18) Read Chapter 4 Section 5
(Posted 9/25) Read Chapter 5.1-5.5. Optional reading 5.6
(Posted 9/25) EXAM REMINDER: The first midterm will be on Wednesday, October 7th during class hours. The exam will include all material that we cover until the end of the lecture on Wednesday, September 30. The exam is closed book and notes, but you are allowed to bring one regular size paper with your notes on both sides. The notes must be handwritten, please do not photocopy and shrink the entire book or my notes on to the paper. No laptops or calculators.
(Posted 10/7) Read Chapter 6.1-6.5 and 6A. Optional reading 6.6 and 6B
(Posted 10/19) Read Chapter 7.1-7.8 and Appendices A,B,C. Optional reading 7.9 and 7.10
(Posted 10/29) Read Chapter 8.1-8.5
(Posted 11/2) Read Chapter 8.6-8.7. Optional reading 8.8-8.10, 8A, 8B and 8C
(Posted 11/3) IMPORTANT: The following questions from the textbook have been added to Assignment 4: Problems 8.8 and 8.10. Also Assignment 4 due date postponed to Wednesday, November 11.
(Posted 11/6) Read Chapter 9.1-9.4. Rest of Chapter 9 is optional.
(Posted 11/10) Read Chapter 10.1-10.4
(Posted 11/10) ) EXAM REMINDER: The second midterm will be Wednesday, November 18th. The exam will include all material we covered until Bayesian Estimation. Bayesian estimatin is excluded. The exam is closed book and notes, but you are allowed to bring one regular size paper with your notes on both sides. The notes must be handwritten, please do not photocopy and shrink the entire book or my notes on to the paper. No laptops or calculators.
(Posted 11/12) Read Chapter 10.5-10.8. Optional reading Appendix 10A.
(Posted 11/19) Read Chapter 11.1-11.6. 

Lecture Notes

(Posted 8/25) Introduction
(Posted 8/25) Minimum Variance Unbiased Estimation
(Posted 9/8) Cramer-Rao Lower Bound (scalar case)
(Posted 9/14) Cramer-Rao Lower Bound (vector case)
(Posted 9/16) Linear Models
(Posted 9/25) Colored Noise
(Posted 9/29) Sufficient Statistics
(Posted 10/7) Best linear unbiased estimation
(Posted 10/19) Maximum Likelihood Estimation
(Posted 10/21) Monte Carlo Matlab script for class example
(Posted 10/27) Expectation Maximization
(Posted 10/29) Least Squares Part I
(Posted 11/2) Least Squares Part II
(Posted 11/4) MATLAB examples: Least squares model order, Sequential Least Squares, Sequential Least Squares with update rule
(Posted 11/6) Method of moments
(Posted 11/10) Bayesian Estimation Introduction
(Posted 11/12) Gaussian Prior
(Posted 11/19) Bayesian MMSE estimator continued

Assignments

(Posted 9/1) Assignment 1
(Posted 9/18) Assignment 2
(Posted 10/21) Assignment 3
(Posted 10/30) Assignment 4 -- MATLAB code for Expectation Maximizaton class example
                        The following questions from the textbook have been added to Assignment 4: Problems 8.8 and 8.10
                        Assignment 4 due date postponed to Wednesday, November 11.
                        Here is my MATLAB code for question 3

Exams and solutions

Midterm 1 and Solutions