Prof. Dr. Moritz Diehl
Modeling 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 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 noncomputer 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
 JiaJie 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 nonerasable pens, paper, a nonprogrammable calculator, and two doublesided A4 pages of selfchosen 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) msiml.ipynb . This is an optional exercise sheet that will provide an introduction to machine learning using Python.
 VOLLEYBALL: code, try casadi