Course Information
Estimation Theory - Fall 2019
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: 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 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
- [11/20] The final exam will be held in class on 12/4 (Wed). 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.
- [10/14] The midterm will be held in class on 10/23 (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.
- [08/26] Please read Ethics of Learning.
Schedule
Week | Reading | Date | Lecture | Date | Lecture |
---|---|---|---|---|---|
1 | Kay Ch. 1 Simon Ch. 1, 2 |
9/2 |
|
9/4 |
|
2 |
|
9/9 |
|
9/11 |
|
3 | Kay Ch. 5, Ch. 6 |
9/16 |
|
9/18 |
|
4 | Kay Ch. 7.1 - 7.6, Ch. 8 | 9/23 |
|
9/25 |
|
5 | Kay Ch. 10, Ch. 11, Ch. 12 | 9/30 |
|
10/2 |
|
6 | Kay Ch. 12 | 10/7 |
|
10/9 |
|
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 |
|
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", 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)