Project works
Tutorials

Software Engineering for Machine Learning Applications in Manufacturing

Event type Tutorial (master´s degree)
Contact person Tobias Biegel M. Sc.
Nicolas Jourdan M. Sc.
Date The tutorial will take place in the winter semester 2022/23 and is divided into a theory and practical part.

Theory part from 21.11.2022 to 29.11.2022 (full-time)
Practical part from 30.11.2022 to 16.11.2022 (part-time)

Written exam on 09.01.2023 (subject to change)
Semester Winter semester
Registration The number of participants is limited to 15 students. If you are interested,
please send an email to first. As soon as you receive a positive response from the organizers by e-mail, you can register in TuCan.
Participation Requirements Prior knowledge of programming with Python is required. Unfortunately, no basic course in programming with Python can be given during the tutorial. Lack of previous knowledge will lead to a higher familiarization effort for the programming tasks during the tutorial. References for familiarization with Python will be sent to the participants before the tutorial starts.
Note Subject to the dynamic Corona situation, the tutorial will be conducted in presence.
For the execution of the exercises in the theory part as well as the processing of the practical part, an own mobile PC/laptop is required.
Intention The aim of the tutorial is to teach students how to use the methods of machine learning and professional software development in the context of production, both theoretically and practically.
Main aims After the students have successfully completed the course unit, they should be able to develop software for solving manufacturing-related problems using machine learning, while complying with the specifications of time, quality and cost.

The students are enabled to:

1. Explain and independently apply methods and tools of professional software development.
  • Use of the Python programming language
    • Basics of object-oriented programming
    • Use of software testing for quality assurance
  • Use of the Git version control software
  • Use of the Linux OS for development

2. Explain and independently apply machine learning methods and tools in the context of production.
  • Apply established process models (CRISP-DM, etc.)
  • Explain and select appropriate machine learning approaches (regression, classification, etc.) for given use cases
  • Explain and select appropriate Deep Learning approaches for given use cases
  • Use of relevant Python libraries in the context of machine learning (NumPy, Pandas, scikit-learn, Keras)

3. Develop and implement selected solutions to problems in the context of manufacturing together as a team.

4. Compile, present and critically evaluate the results.
Activities Theory part from 21.11.2022 to 29.11.2022 (full-time)
In the first week and a half of the tutorial, students are taught the basics for working on the practical project. The contents of the theory part will be examined in a 30-minute written exam. The written exam will take place on 09.01.2023.

Practical part from 30.11.2022 to 16.11.2022 (part-time)
In the practical part, the knowledge imparted in the theory part is applied using a real application from production in group work. During the practical part, office hours will be offered. Day and time are not yet fixed and will be announced. The practical part ends with the presentation of the project results in the form of an oral examination.