KIIng
Table of contents
Important data at a glance
| Duration: | 10/2020 – 03/2022 |
|---|---|
| Funding: | BMBF KI-Campus Ideenwettbewerb |
| Processor / Contact person: | Dr. rer. nat. Michael Schwarzenberger |
|
Cooperation: |
none |
| Logline: | The aim of this project is to develop a holistic AI learning offering on the AI Campus specifically for the engineering sector. The application-oriented e-learning courses teach everything necessary from data interfaces to machine learning and deep learning. |
Objective
Motivation
The digital transformation in industry, keyword Industry 4.0, enables applications such as cyberphysical production systems, smart manufacturing and predictive maintenance. For successful implementation, domain knowledge is essential in addition to knowledge of artificial intelligence (AI). Therefore, engineers need to be educated and trained in the field of AI to enable the successful application of AI in the industrial environment. To cope with these new requirements, engineers need comprehensive knowledge – not only in the field of AI, but also in accompanying activities such as data acquisition from machines and sensors.
Solution approach
For this reason, the KIIng project relies on an overarching treatment of the topic in the form of the Micro Degree "Holistic Applied AI in Engineering" at KI-Campus.org. Linking AI teaching with the content of process informatics is necessary for the successful, holistic management of AI projects. Therefore, the proposed Micro Degree covers the three main topics of Process Informatics, Machine Learning, and Deep Learning with Process Data through three corresponding sub-courses.
Results
The Micro Degree will be published on the learning platform KI-Campus.org and will then be available to learners free of charge. The course materials will be created as OER (Open Educational Resource under CreativeCommons license). Furthermore, this will expand the range of courses offered at TU Dresden so that students of mechanical engineering and other engineering sciences can also experience a holistic and application-oriented education in the field of AI.
Contact
© Crispin-Iven Mokry
Research associate
NameMr Dr. rer. nat. Michael Schwarzenberger
Process Informatics and Machine Data Analysis
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Chair of Machine Tools Development and Adaptive Controls
Visitor Address:
Kutzbach-Bau, Room E6 Helmholtzstraße 7a
01069 Dresden