Introduction to Productivity, Flexibility and Team Work

Increase your Productivity

Schedule your weekly tasks, meetings, courses or activities!

Increase your Flexibility

Access your files from any computer, tablet or phone!

Work as a Team

Edit together in real-time with easy sharing, and use comments, suggestions, and action items to keep things moving. Or use @-mentions to pull relevant people, files, and events into your online files for rich collaboration.

Important Links

17.10.2022 – Introduction to CAD Software

Why do I need CAD Software?

  • Computer-Aided Design (CAD) is the cornerstone of how you design and build things. It allows the user to digitally create, visualise, and simulate 2D or 3D models of real-world products before it is being manufactured.
  • CAD models allow users to iterate and optimize designs to meet design intent.
  • The use of CAD software facilitates the testing of real-world conditions, loads, and constraints, which increases the quality of the product.
  • CAD software helps to explore ideas and visualise the concept.
  • Improve the quality, precision of the design, and communication in the design process.
  • Analyse real-world scenarios by computer-aided analysis
  • Create a database for product development and manufacturing.

Some Practical Applications of CAD Software


Automobile parts can be modelled, visualised, revised, and improved on the screen before being manufactured.

Electrical schematics, control circuit diagrams, PCBs, and integrated circuits (ICs)  can be designed and developed with ECAD software 

With CAD software, architects can visualise and simulate their entire project using real-world parameters, without needing to build any physical structuress or models. 

What CAD software do I need?

Something free

  • FreeCAD
  •  TinkerCAD
  • Fusion 360
  • Onshape
  • Solid Edge
  • Blender
  • SketchUp

My design goes with me wherever I go (cloud-based)

  • Onshape
  • TinkerCAD
  • AutoCAD Web
  • SelfCAD
  • Vectary
  • SketchUp

Something more advanced and professional

  • AutoCAD
  • Autodesk Inventor
  • SolidWorks
  • Fusion 360
  • Solid Edge
  • Onshape
  • Shapr3D
  • Creo

Windows OS

  • AutoCAD
  • Autodesk Inventor
  • Solidworks
  • Fusion 360
  • Creo
  • Solid Edge
  • Shapr3D
  • Blender

Linux OS

  • NX Advanced Designer
  • Blender


  • AutoCAD
  • Autodesk Inventor
  • Fusion 360
  • Shapr3D
  • Blender
  • NX Advanced Designer

iOS, Android

  • AutoCAD
  • Autodesk Inventor
  • Shapr3D

Where can I learn CAD?

Introduction to Python

Why should you consider learning Python

  • Python is the most popular programming language in the world with a popularity of 28%.
  • It is easier to learn than many other programming languages.
  • Python is very readable due to its structure. Thus, bugs can usually be found and fixed quickly.
  • Python can be both procedural and object-oriented, which makes it very versatile.
  • There is a large number of software libraries. On python package index, PyPI, over 400,000 packages can be found. And the most important ones can be downloaded quickly and easily using pip.
  • Python and almost all Python software libraries are available for free on the Internet.

What Python can be used for

  • Data Science is one of the most popular application areas of Python. Here, not only the complete data analysis but also the visualization will be implemented in Python. Generally, software libraries such as Numpy, Matplotlib and Pandas are used for this purpose.
  • Machine learning is another very popular area. Python can be used for supervised, unsupervised and reinforcement learning. Libraries such as TensorFlow, Keras, PyTorch or scikit-learn are used for this purpose.

Furthermore, Python can be used for all kinds of general purpose programs, such as app development, GUI design, web development and in many other areas. In addition, programs with Python interfaces can be automated through a Python program, for example simulation programs.

Practical example of Python in academia

Especially during the time as a student it is good to have a tool with which you can validate your hypotheses or perform the calculations by the computer. Thus, programs can be written with which problems in physics, chemistry or other tasks can be simulated and subsequently the simulation data can be plotted or even videos can be generated.

Important links

Understanding the basics of privacy

Important Articles

Companies like Google collect and process your data

Google collects your data from many different sources. Here are some examples:

  • Gmail: Google can read and store information from every email you write and receive, including in the spam, draft, and trash folders.
  • Google Maps: Google saves every location you search, in addition to all the places you physically visit with your devices, even if you aren’t logged in. Are you using Waze instead? Google owns that too. The ubiquity of phones and our constant use of them makes them almost like tracking devices we carry around willingly.
  • Android devices: Because Android phones and tablets run on an operating system built by Google, the company can track which ads you’re shown while using your phone. Google also knows what time, down to the second, you open each app.
  • Google apps: The Google Play store records all your searches and downloads, as well as any rewards cards used. Google also tracks which articles you’ve read through Google News.
  • YouTube: Google acquired YouTube back in 2006. When you’re using YouTube, Google tracks your search history, your watch history, how long you spend watching videos, and all your comments and likes or dislikes.
  • Google Assistant: Every request you make and every question you pose is recorded — you can even listen to the audio playback.
  • G Suite: Your calendar shows where you’ll be and when, and Google Hangouts saves all of your conversations.

If you are interessted in which data Google has collected about you, test Google Takeout.

Recommendations: Browser, Search Engine & Online Docs

In our digital age, we have to be aware of the data collection strategies of all services that we use. However, often, alternatives  developed by the open-source community exist. Here are some recommendations:


Recommendations: Messenger & Repositories

  • I personally recommend: Nextcloud’s Talk App.
  • Setup your own repo server using, e.g., Gitea or Gitey.

Final remarks: Stay sensitive to what happens to your data. Nothing is for free.

150.000 MINT – Digital competencies (0.66SH P, WS)

This entry course discusses major competences all students should have to study at the MUL.
The Chair of CPS provides tutorials on
  • Data Safety, Privacy and Content Search on the net.
  • Learning Python using online tools.
  • Learning to develop 3D-CAD models using online tools.
  • Using powerful online team working tools including shared documents.
  • Using data repositories and creating your personal webpage.

Links and Resources

Location & Time

  • See the MUOnline link. 
  • CPS Presentations are on the: 17.10.2022 at 11.00 in the HS1 Studierendenzentrum.

Posts on Digital Competencies

190.002 Cyber-Physical Systems Lab (2SH P, WS)

The exercise will enable the application of modern machine learning techniques and tools in robotics / cyber-physical systems. The following topics will be covered in the course:
   – Kinematics, dynamics & simulation of CPS.
   – Data representations & model learning.
   – Control techniques, priorities & torque control.
   – Planning & cognitive reasoning.
   – Reinforcement learning and black-box optimization.

The course provides a structured and well motivated overview over modern techniques and tools which enable the students to define learning problems in Cyber-Physical-Systems. 

Links and Resources


Assignments / Team work

Location & Time

190.001 Cyber-Physical Systems (2SH L, WS)

This lecture provides a unique overview over central topics in Cyber-Physical-Systems:

  1. Kinematics, Dynamics & Simulation of CPS
  2. Data Representations  & Model Learning
  3. Feedback Control, Priorities & Torque Control
  4. Planning & Cognitive Reasoning
  5. Reinforcement Learning & Policy Search

The course provides a structured and well motivated overview over modern techniques and tools which enable the students to define learning problems in Cyber-Physical-Systems. 

Links and Resources


Exam Preparation & Q&A’s

  • 13.01.2023 10:15 [Online only] Discussion of student questions regarding the exam.
  • 20.01.2023 10:15 [Online only] Course Feedback & Discussion of student questions regarding the exam.
  • 27.01.2023 10:15 [Online only] Discussion of student questions regarding the exam.
  • 30.01.2023 10:15 HS Thermoprozesstechnik Written Exam 
  • 06.03.2023 10:15 Location not fixed Written Exam (2nd option)

Last Year’s Slides:

Exam Dates

  • 30.01.2023 at 10:15 in HS Thermoprozesstechnik.
  • 06.03.2023 at 10:15 in HS Thermoprozesstechnik.
  • upon request via

Location & Time

  • Location: HS Thermoprozesstechnik
  • Dates: Mondays, 10:15 – 12:00

Learning objectives / qualifications

  • Students get a comprehensive understanding of Cyber-Physical-Systems.
  • Students learn to analyze the challenges in simulating, modeling and controlling CPS.
  • Students understand and can apply basic machine learning and control  techniques in CPS.
  • Students know how to analyze the models’ results, improve the model parameters and can interpret the model predictions and their relevance.

Programming Assignments & Simulation Tools

For simulating robotic systems, we will use the tool CoppeliaSim. The tool can be used for free for research and for teaching. 

To experiment with state of the art robot control and learning methods Python will be used. If you never used Python and are unexperienced in programming, please visit the tutorials on Python programming prior to the lecture.  

The course will also use the tool Code With Me from JetBrains. With this stool, we can develop jointly code. 


  • The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 
  • Bishop 2006. Pattern Recognition and Machine Learning, Springer. 
  • Barber 2007. Bayesian Reasoning and Machine Learning, Cambridge University Press
  • Murray, Li and Sastry 1994. A mathematical introduction to robotic manipulation, CRC Press. 
  • B. Siciliano, L. Sciavicco 2009. Robotics: Modelling,Planning and Control, Springer.
  • Kevin M. Lynch and Frank C. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.

190.013 Introduction to Machine Learning Lab (2SH P, SS)

This exercise is part of the lecture 190.012 Introduction to Machine Learning

Enrolling for this exercise is a highly recommended but not a requirement for passing the machine learning lecture. 

Course Resources

Python Videolectures and Tutorials

Location & Time


Course Topics

The exercise is based on multiple short (typically 2-4 pages) assignments. For most assignments a written report in Latex and Python Code has to be submitted via Email. Each student has to submit an individual assignment report and code.

The topics of the assignments are

  • Creating Latex Documents & Data handling (Latex env. setup, report template, reading & editing data files),
  • Programming in Python & Probability Theory (Python basics, Editor PyCharm, Workflow, variables, functions, classes, plotting, Gaussian distributions, sampling, plotting),
  • Linear Probabilistic Regression (features, least-squares regression derivation & implementation, regularization),
  • Nonlinear Probabilistic Regression (Gaussian Processes, implementation, kernels, predictions)
  • Probabilistic Time-Series-Models (Implementation & Learning from real-world data, Visualization, Predictions).
Details to the grading will be presented in the first exercise on the 22.02.2022. For each assignment code templates will be provided.

Learning objectives / qualifications

Hands-on experience with machine learning methods.

190.012 Introduction to Machine Learning (2SH L, SS)

This course is based on the Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 

This book presents fundamental theories, algorithms and concepts of probabilistic
machine learning techniques. It is written for experienced undergraduate or first
semester graduate students.

Links and Resources

Location & Time



Course Topics

  1. Introduction to Machine Learning (Data and modelling fundamentals)
  2. Introduction to Probability Theory (Statistics refresher, Bayes Theorem, Common Probability distributions, Gaussian Calculus).
  3. Linear Probabilistic Regression (Linear models, Maximum Likelihood, Bayes & Logistic Regression).
  4. Nonlinear Probabilistic Regression (Radial basis function networks, Gaussian Processes, Recent research results in Robotic Movement Primitives, Hierarchical Bayesian & Mixture Models).
  5. Probabilistic Inference for Time Series (Time series data, basis function models, learning).

Learning objectives / qualifications

  • Students get a comprehensive understanding of basic probability theory concepts and methods.
  • Students learn to analyze the challenges in a task and to identify promising machine learning approaches.
  • Students will understand the difference between deterministic and probabilistic algorithms and can define underlying assumptions and requirements.
  • Students understand and can apply advanced regression, inference and optimization techniques to real world problems.
  • Students know how to analyze the models’ results, improve the model parameters and can interpret the model predictions and their relevance.
  • Students understand how the basic concepts are used in current state-of-the-art research in robot movement primitive learning and in neural planning.


The course will be graded based on a written exam. 50% of all questions need to be answered correctly to be positive. The exam will take place either online (via Moodle) or in the class room, depending on the current university regulations.

Furthcoming exam dates are:


  • The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 
  • Daphne Koller, Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. ISBN 978-0-262-01319-2
  • Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer (2006). ISBN 978-0-387-31073-2.
  • David Barber. Bayesian Reasoning and Machine Learning, Cambridge University Press (2012). ISBN 978-0-521-51814-7.
  • Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. ISBN 978-0-262-01802-9