Prof. Dr. Moritz Diehl
Modeling and System Identification (MSI) is concerned with the search for mathematical models for real-life systems. The course is based on statistics, optimization and simulation methods for differential equations. The exercises will be based on pen-and-paper exercises and computer exercises with MATLAB.
Lectures
Lectures take place on Wednesdays 08:30h to 9:55h and Fridays 10:05 to 11:50, in HS 00 036 (Schick - Saal) in building 101.
Recordings of some of the lectures are available on the webpage of the video center.
Course material is the following:
- MSI script (updated October 23, 2019) by Prof. Diehl,
- Script by Prof. Johan Schoukens, VUB, Brussels, Belgium,
- Textbook, Ljung, L. (1999). System Identification: Theory for the User. Prentice
Hall. Available in the campus library.
Tentative course schedule (may change, please check regularly):
Date | Topic | (Past) Recordings |
---|---|---|
October 23 | Complete lecture | |
October 25 | Linear Algebra Tutorial | |
October 30 | Complete lecture | |
November 6 | Statistics Tutorial | Complete tutorial |
November 8 | Complete lecture | |
November 13 | Complete lecture | |
November 15 | Complete lecture | |
November 20 | Complete lecture | |
November 22 | Complete lecture | |
November 27 | ||
November 29 | ||
December 4 | Complete lecture | |
December 6 | Microexam 1 & Solution | Complete lecture |
December 11 |
no lecture |
no recordings |
December 13 | No new recordings for this winter term. Please watch the old recordings from 2017/18. |
|
December 18 | ||
December 20 | no lecture | |
January 8 | ||
January 10 |
Machine Learning in a Nutshell (Slides ) |
|
January 15 | ||
January 17 | ||
January 22 | Complete lecture | |
January 24 | Microexam 2 & Solution | Complete lecture |
January 29 | ||
January 31 | code | Complete lecture |
February 5 | Complete lecture | |
February 7 | Summary Lecture | no recordings |
February 12 | Microexam 3 & Solution | no recordings |
February 14 | Q&A session | no recordings |
Exercises
Exercise sessions are organized on (starting on October 24, 2019):
- Thursday 16:00 to 18:00
- Friday 12:00 to 14:00
- Tuesday 12:00 to 14:00
in building 082, room 029.
Please hand in solutions to computer exercises through Matlab Grader individually (you should have received an invitation email, email us). Solutions to non-computer exercises can be handed in on paper by groups of maximum 3 persons during the Wednesday lecture or before that in building 102, 1st floor, 'Anbau' (here). The corrected exercises will be handed out during the exercise sessions.
Exercise files:
- Exercise 0 (updated)
- Exercise 1 dataset
- Exercise 2 (updated)
- Exercise 3 dataset
- Exercise 4 (updated)(updated) dataset
- Exercise 5 dataset
- Exercise 6 dataset
- Exercise 7 dataset
- Exercise 8 (updated) dataset_task1 dataset_task2
- Exercise 9 (updated)(updated)
- Exercise 10 dataset
- Exercise 11 dataset
In order to pass the exercises accompanying the course (`Studienleistung`), one has to obtain at least 20 exercise points in each of the three blocks:
- Block: Exercises 0 - 3 + Microexam 1,
- Block: Exercises 4 - 7 + Microexam 2, and
- Block: Exercises 8 - 11 + Microexam 3.
After each Microexam we will provide an anonymous list of the number of exercise points as well as the result of the microexam of each student here (1. Block , 2. Block , 3.Block&Total ). If you are interested in your current number of exercise points, send us an email at any time or ask us at the exercise sessions.
If you have any questions regarding the exercises, email us.
Teaching Assistants
- Tobias Schöls
- Jia-Jie Zhu
- Naya Baslan
- Jakob Harzer
- Bryan Ramos
If you have questions please approach us during the exercise sessions. In urgent cases you may also send an email to syscop.msi@gmail.com
Final Exam
The final exam will take place on March 20, 2020 at 14.00h in lecture halls 026 + 036 in building 101.
UPDATE (March 30, 2020): The final exam has been rescheduled to April 24, 14.00 to 17.00h, in Audimax (building KG II) lecture halls HS 026 + HS 036 in building 101 and room 006 "Kinohörsaal" in the mensa building 082.
UPDATE (APRIL 23, 2020): The final exam has been rescheduled to April 24, 14.00 to 17.00h, in Audimax (building KG II). Please bring your Student ID, a Photo ID (Personalausweis, Passport, Driver's license, or similar), a mask (or something similar to cover your mouth and nose), and a signed corona leaflet (please read it carefully, before signing, see also information by the University). We will start admitting people into the building around 13.30h from the theater side of the building (Platz der Alten Synagoge). Please arrive early, keep a distance of at least 1.5 meters and wear a mask while you wait in line.
A sample exam can be found here (solution).
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 non-erasable pens, paper, a non-programmable calculator, and two double-sided A4 pages of self-chosen formulae are allowed.
Tutorials
The material for the tutorials:
MATLAB
We recommend students to install MATLAB on their laptop and bring it to the exercise sessions (and the tutorials in the beginning of the semester). The university provides licences.
There is an online (in browser) version of MATLAB. This service is provided by MathWorks and can be accessed with a MathWorks account. We won't be able to provide support for the online version and the exercises may exceed its capabilities.Ultimately MATLAB is installed on some come computers in the computer pool. We have no influence on this installation please refer to the pool managers for details.
EXTRA EXAMPLES AND RIDDLES
- THE MOVING BLACKBOARD RIDDLE: The plot and the corresponding MATLAB datafile shows the recorded values of control input values (-1,0,1) for a (virtual) electrically actuated blackboard for a timespan of 60 seconds on the top plot. The lower plot shows measurements of the height (i.e., the output) of the blackboard for the first 40 seconds (in meters). QUESTION: Model the dynamics of the system, identify the relevant parameters, and predict the height of the blackboard at time t=60 s.
- MACHINE LEARNING OPTIONAL EXERCISE SHEET : (Jupyter Notebook) msi-ml.ipynb . This is an optional exercise sheet that will provide an introduction to machine learning using Python.
- VOLLEYBALL: code, try casadi