Course round 2019

Here, you will find information for the course round Fall 2019.

General Information

• The first part of the course coincides with the MSc level course EQ2810 Estimation theory, accelerated program course, 6 cr.
• Course responsible and examiner: Magnus Jansson
• Where: click on "Calendar" to find out.
• When: Period 1-(2) every odd year, see "Calendar" for the schedule. 
• Course literature: "Fundamentals of Statistical Signal Processing: Estimation Theory," Kay, Steven M. ISBN 0133457117.
• Grading: Pass/Fail

Course requirements

• Weekly homework assignments, to be solved and reported individually
• Peer grading of homework assignments
• Two project assignments
• 48 hour take-home examination

Course contents can be learnt by cooperative discussions, but homework problems should be solved
individually and handed in in due time for grading. Please recall the KTH rules for examination.

For passing the course we require 80% of the total score on homework and project assignments and 50% on exam as well as a serious completion of the peer grading task.   

Preliminary Schedule

See KTH central schedule for EQ2810 for the official times and lecture rooms. The Calendar on this page should also be up to date. We have seven time-slots scheduled, Tuesdays 13.15-15.
Preliminary we will use six of them for normal lectures:
Lec. 1: Ch. 1-3   Lecture 1 (PDF)   HW1 (PDF) (due 2019-09-03)
Lec. 2: Ch. 4-5   CRB notes (PDF)  Lecture 2 (PDF)  HW2 (PDF) (due 2019-09-10)
Lec. 3: Ch. 6-7  
Lec. 4: Ch. 8-9 
Lec. 5: Ch. 10-11 
Lec. 6: Ch. 12,14,15
Lec. 7: Reserve, project?
We may need to add some slots later for project presentations
or the like.

Outline of the book

Ch. 1: Introduction
Ch. 2: Minimum Variance Unbiased Estimation
Ch. 3: Cramer Rao Lower Bound
Ch. 4: Linear Models
Ch. 5: General Minimum Variance Unbiased Estimation
Ch. 6: Best Linear Unbiased Estimators
Ch. 7: Maximum Likelihood Estimation
Ch. 8: Least Squares
Ch. 9: Method of Moments
Ch. 10: The Bayesian Philosophy
Ch. 11: General Bayesian Estimators
Ch. 12: Linear Bayesian Estimators
Ch. 14: Summary of Estimators
Ch. 15: Extensions for Complex Data and Parameters

 (Ch. 13: Kalman filtering - will not be covered in this course, see Adaptive signal processing or Optimal filtering courses)