Course Information
Estimation Theory - Fall 2020
Instructor: Prof. Songhwai Oh (오성회) Email: songhwai (at) snu.ac.kr Office Hours: Friday 2:00-4:00PM Office: Building 133 Room 405 |
Course Number: 430.714 Time: MW 2:00-3:15 PM Location: Online (Building 301 Room 103) |
TA: Timothy Ha (하디모데) Email: timothy.ha (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 Cramer-Rao 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, multi-target tracking, and Gaussian process regression. Lectures will be in English.
Announcements
- [12/02] The final exam will be held in class on 12/14 (Mon). The exam is closed-book but you can bring one sheet (A4) of hand written notes on both sides. You have to turn in this cheat sheet with your exam.
- [10/13] The midterm will be held in class on 10/21 (Wed). The exam is closed-book but you can bring one sheet (A4) of hand written notes on a single side (the other side must be blank). You have to turn in this cheat sheet with your exam.
- [08/28] Please read Ethics of Learning.
Schedule
Week | Reading | Date | Lecture | Date | Lecture |
---|---|---|---|---|---|
1 | 9/2 |
|
|||
2 | Kay Ch. 1 Simon Ch. 1, 2 |
9/7 |
|
9/9 |
|
3 |
|
9/14 |
|
9/16 |
|
4 | Kay Ch. 4, Ch. 5 |
9/21 |
|
9/23 |
|
5 | Kay Ch. 6, Ch. 7.1 - 7.6 | 9/28 |
|
9/30 |
|
|
|||||
6 |
Kay Ch. 8, Ch. 10 |
10/5 |
|
10/7 |
|
7 | Kay Ch. 11 | 10/12 |
|
10/14 |
|
8 |
Kay Ch. 12 |
10/19 |
|
10/21 |
|
9 | 10/26 |
|
10/28 |
|
|
10 | Simon Ch. 5, Ch. 6 | 11/2 |
|
11/4 |
|
11 | Simon Ch. 7, Ch. 9 | 11/9 |
|
11/11 |
|
12 | Simon Ch. 9, Ch. 13 | 11/16 |
|
11/18 |
|
13 | Simon Ch. 14, Ch. 15 | 11/23 |
|
11/25 |
|
14 | 11/30 |
|
12/2 |
|
|
15 | 12/14 |
|
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", Wiley-Interscience, 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
- Cramer-Rao 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 multi-target tracking
- *Gaussian process regression (*if time permits)