Prof. Moritz Diehl, Jakob Harzer, Katrin Baumgärtner
Modelling and System Identification (MSI) is concerned with the search for mathematical models for reallife systems. The course is based on statistics, optimization and simulation methods for differential equations. The exercises will be based on penandpaper exercises and computer exercises with python.
Course language is English and all course communication is via this course homepage.
If you have any questions regarding the exercises/lectures, please send an email to the tutors, syscop.msi@gmail.com
Lectures. The lectures will take place on Mondays, 8:10  10:00 a.m and Wednesdays, 9:0010:00 a.m., in Building 101, HS 036. If you cannot attend, you may watch the lecture recordings, see below.
Exercises. The exercise sheets include both penandpaper exercises as well as programming exercises using python. Exercise sheets can be handed in during the lecture on Monday. Programming exercises are handed in via Ilias. You have one week to work on the sheet and you might work in groups of at most three students.
Exercise Sessions are on
 Tuesday, 15:1516:15, building 102, room 02011
 Wednesday, 10:0011:00, building 78, room SR 00 014
 Wednesday, 10:0011:00, building 102, room 02012
During the exercise session, the exercise solutions are discussed. Afterwards there is room for questions on the current exercise sheet.
Written material. The lecture closely follows the script, which can be found below:
 Lecture notes on Modelling and System identification: PDF
If you missed the first lecture, you can pick up a printout of the script in Building 102, in the cupboard in front of room 00 075.
Please note that we do not cover Chapter 8.4. Additional material that covers some of the lecture contents:
 A script by Johan Schoukens (Vrije Universiteit Brussel, Belgium), which can be found here.
 The textbook Ljung, L. (1999). System Identification: Theory for the User. Prentice Hall. This book is available in the faculty library.
Final Evaluation and Microexams
Please make sure you register for both the MSI Exam and the MSI Studienleistung!
The final grade of the course is based solely on a final written exam at the end of the semester. The final exam is a closed book exam, only pencil, paper, and a calculator, and two handwritten doublesided A4 sheets of selfchosen formulae are allowed.
Each exercise sheet gives a maximum of 10 points. Three online microexams written during some of the lecture slots give a maximum of 10 exercise points each. In order to pass the exercises accompanying the course (Studienleistung), one has to obtain at least 50% of the maximum exercise points in each of the three blocks:
 Block 1: Exercises 1  3 + Microexam 1 (total 40 points)
 Block 2: Exercises 4  6 + Microexam 2 (total 40 points)
 Block 3: Exercises 7  10 + Microexam 3 (total 40 points + 10 Bonus Points)
To prepare for the written exam, check out the exams from previous semesters: 2018, 2015, 2014. (Please note that these exams contain questions on Appendix C of the MSI script, which is not covered in this year's lecture)
Solution video for the 2018 exam
Lectures and Microexams
Monday, October 17  Intro session 
Wednesday, October 19  Lecture 
Monday, October 24  Lecture 
Wednesday, October 26  Lecture 
Monday, October 31  Lecture 
Wednesday, November 2  Lecture 
Monday, November 7  Lecture 
Wednesday, November 9  Lecture 
Monday, November 14  Lecture 
Wednesday, November 16  Lecture 
Monday, November 21  Lecture 
Wednesday, November 23  Lecture 
Monday, November 28  Microexam online, 9:0010:00 
Wednesday, November 30 
Talk by Andrea Ghezzi: "A real estimation problem in the steel industry" 
Monday, December 5  Lecture 
Wednesday, December 7  Lecture 
Monday, December 12  Lecture 
Wednesday, December 14  Lecture 
Monday, December 19  Lecture 
Wednesday, December 21  Microexam online, 9:0010:00 
CHRISTMAS BREAK  
Monday, January 9  Lecture 
Wednesday, January 11  Lecture 
Monday, January 16  Lecture 
Wednesday, January 18  Lecture 
Monday, January 23  Lecture: Covariance, Continuous Time, Numerical Integration, + 
Wednesday, January 25  Microexam online, 9:0010:00 
Monday, January 30 
Lecture: Explanation of Past Exam (reallife and recorded) + EKF Lighthouse CrazyFlie Drone Example by Mohammed Hababeh 
Wednesday, February 1  Guest Lecture by Moritz Berger “Bayesian Sensor Calibration” (Link to Paper Bayesian Sensor Calibration) 
Monday, February 6  Lecture: Explanation of Past Exam (reallife and recorded) + Rotokite Demo (by Paul Krüger and Simon Hettich) 
Wednesday, February 8  Lecture: Past Exam (cont., recorded) and Summary 
Lecture Recordings
date  topic  chapters 
October 17  October 21  Lecture 1: Introduction + Resistance Estimation 
11.2 
October 24  October 28  Lecture 2: Resistance Estimation + Statistic Basics 
1.2.22.3 
October 24  October 28  Lecture 3: Random Variables + Statisitical Estimators 
2.32.4 
October 31  November 4  Lecture 4: Resistance Estimation Revisited 
2.53.1 
November 7  November 11  Lecture 5: Optimization Basics + Linear Least Squares 
3.14.2 
November 7  November 11  Lecture 6: WLS + Illposed Problems 
4.34.4.1 
November 14  November 18  Lecture 7: Statistical Analysis of WLS 
4.54.7 
November 14  November 18  Lecture 8: Maximum Likelihood Estimation 
55.1.1 
November 21  November 25  Lecture 9: MAP Estimation + Recursive LLS 
5.25.3.2 
December 5  December 9  Lecture 10: Cramer Rao Bound (the part on Section 5.4: Cramer Rao Bound starts at 38min) (Section 5.4.1: Proof of Cramer Rao Bound. Note that the proof is not required for the exam) 
5.35.4 
December 12  December 22  Lecture 11: Practical Solution of NLS 
5.5. 
January 9  January 13  Lecture 12: Dynamic systems (Part1, Part2, Part3) (2,5 hours in total) 
6.16.1.2 
January 16  January 20  Lecture 13: Output and Equation Errors (1h) 
7.17.3 
January 23  January 27  Lecture 14: State Space Models (0,5h) 
7.4 
January 23  February 27  Lecture 15: RLS + Kalman Filter (1h) 
9.19.3 
January 30  February 3  Lecture 16: Extended Kalman Filter (1h) 
9.5 
January 30  February 3  Lecture 17: Moving Horizon Estimation (1,5h) 
9.6 
Exercises Sheets
Sheet  Material  Deadline 
Sheet 0: Intro  October 24  
Sheet 1: Resistance Estimation Example  Material Ex 1  October 31 
Sheet 2: Statistics + Parameter Estimation  Material Ex2  November 7 
Sheet 3: Optimality Conditions and Linear Least Squares  Material Ex3 (corrected)  November 14 
Sheet 4: Weighted Linear LeastSquares  Material Ex4  November 21 
Sheet 5: IllPosed Linear LeastSquares & Regularization  Material Ex5 (corrected)  December 5 
Sheet 6: Maximum Likelihood and MAP Estimation  Material Ex6  December 12 
Sheet 7: Recursive Least Squares  Material Ex7  January 9 
Sheet 8: Nonlinear Least Squares  Material Ex8  January 16 
Sheet 9: Kalman Filter  Material Ex9  January 30 
(BONUS) Sheet 10: Extended Kalman Filter  Material Ex10  February 6 
Tutorials
In the first week, there is no mandatory exercise sheet, but if you don't feel too confident about your linear algebra and statistics skills, you might want to check out these tutorials that cover the basics needed for the MSI course.
 Python Tutorial  for more information see the paragraph below
 Linear Algebra Tutorial
 Statistics Tutorial
In the second week, the tutors will discuss the solutions to the tutorials in the exercise sessions.
Python
For the programming exercises we use Python. To work on the exercises please make sure to have Python installed on your system.
Python Installation
Here is a short guide on how to set up Python along with the IDE VS Code. If you already have Python installed on your system or want to use another IDE, feel free to skip to bullet 4.

Install Python for your operating system

Install VS Code

Install the Python Extension for VS Code

Install the required python packages:

Type and press enter:
pip install numpy scipy matplotlib pytest
Python Tutorial Notebooks
For people who do not know Python or want to refresh their knowledge, we provide a series of Jupyter notebooks to give you an introduction to data science programming in python. More resources, such as video tutorials for Python can be found online.

Download and unzip the Tutorial Notebooks into a folder of your choice

Open the folder in VS Code (File > Open Folder)

Open the first Notebook by going through the file tree (notebooks/1basics/PY0101EN11Hello.ipynb)
Blackboard Photos
First Draft of Complete Rotokite Model Equations (9.11.2023) IMG_5512.HEIC.pdf