Course Information
Instructor: Prof. Songhwai Oh (오성회) Email: songhwai (at) snu.ac.kr Office Hours: Friday 2:004:00PM Office: Building 133 Room 405 
Course Number: 430.714 Time: MW 2:003:15 PM Location: Building 302 Room 408 
TA: Chanho Ahn (안찬호) Email: chanho.ahn (at) rllab.snu.ac.kr Office: Building 133 Room 610 
Course Description
This course introduces classical and modern topics in estimation theory to graduate level students. Topics include minimum variance unbiased estimators, the CramerRao bound, linear models, sufficient statistics, best linear unbiased estimators, maximum likelihood estimators, least squares, exponential family, multivariate Gaussian distribution, Bayes risk, minimum mean square error (MMSE), maximum a posteriori (MAP), linear MMSE, sequential linear MMSE, Bayesian filtering, Kalman filters, extended Kalman filter, unscented Kalman filter, particle filter, data association, multitarget tracking, and Gaussian process regression. Lectures will be in English.
Announcements
 [11/20] The final exam will be held in class on 12/4 (Wed). The exam is closedbook but you can bring one sheet (A4) of handwritten notes on both sides. You have to turn in this cheat sheet with your exam.
 [10/14] The midterm will be held in class on 10/23 (Wed). The exam is closedbook but you can bring one sheet (A4) of handwritten notes on a single side (the other side must be blank). You have to turn in this cheat sheet with your exam. Previous midterms: 2018, 2017.
 [08/26] Please read Ethics of Learning.
Schedule
Week  Reading  Date  Lecture  Date  Lecture  Assignment 

1  Kay Ch. 1 Simon Ch. 1, 2 
9/2  9/4  
2 

9/9  9/11 
HW 1 (due: 9/18, in class) Kay 3.1, 3.2, 3.4, 3.9, 4.1, 4.5 

3  Kay Ch. 5, Ch. 6 
9/16  9/18  
4  Kay Ch. 7.1  7.6, Ch. 8  9/23  9/25 
HW 2 (due: 10/2, in class) Kay 5.3, 5.6, 5.9, 6.1, 6.2, 7.1 

5  Kay Ch. 10, Ch. 11, Ch. 12  9/30  10/2  
6  Kay Ch. 12  10/7  10/9 

HW 3 (due: 10/14, in class) Kay 8.6, 8.12, 10.10, 10.12, 11.3 

7  Simon Ch. 5  10/14  10/16  
8  Simon Ch. 6  10/21  10/23 


9  Simon Ch. 7  10/28  10/30 


10  11/4 

11/6 


11  Simon Ch. 9  11/11  11/13 
HW 4 (due: 11/20, in class) Simon 5.2, 5.5, 5.7, 6.1, 6.12, 7.4 

12  Simon Ch. 13, Ch. 14  11/18  11/20  
13  Simon Ch. 15  11/25  11/27  
14  12/2  12/4 

Textbooks

[Recommended] Steven M. Kay, "Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory", Prentice Hall, 1993.
 [Recommended] Dan Simon, "Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches", WileyInterscience, 2006.
Prerequisites
 Students must have a solid background in linear algebra, linear system theory, and probability.
Topics
 Introduction and review of probability and linear system theory
 Minimum variance unbiased estimators
 CramerRao lower bound
 Linear models and sufficient statistics
 Best linear unbiased estimators and maximum likelihood estimators
 Least squares, exponential family, and Bayesian approaches
 Multivariate Gaussian distribution
 Bayes risk, minimum mean square error (MMSE), and maximum a posteriori (MAP)
 Linear MMSE and sequential linear MMSE
 Bayesian filtering
 Kalman filtering
 Advanced topics in Kalman filtering
 Extended Kalman filter, unscented Kalman filter, and particle filter
 *Data association and multitarget tracking
 *Gaussian process regression (*if time permits)