Studies

Project on Applied AI

Studies

 

Project on Applied AI in Factory Automation and Production Systems (AI-FAPS)

Module content

For students of the master programs “Artificial Intelligence”, “Data Science”, and “Computational Engineering”, we offer project topics that are related to our current research in applying AI in industry. Other than a course with a fixed topic, project topics are defined individually. Possible topics include the application of cutting-edge AI methods in industrial areas such as

  • electromechanical engineering,
  • electronics production,
  • signal and power networks,
  • automation technology,
  • engineering systems,
  • medical technology,
  • robotics,
  • or home automation

and are derived from the various industry-related research projects of the institute.

Depending on the topic, subsymbolic AI methods (e.g., machine learning, deep learning, reinforcement learning), symbolic AI methods (e.g., knowledge-based representation and reasoning), or a combination thereof can be used. Considering the current state of the art as well as the respective problem definition, a suitable AI approach is to be developed and evaluated.

Target group

The 10 ECTS project is directed to students of the master programs “Artificial Intelligence (M.Sc.)”, “Data Science (M.Sc.), and “Computational Engineering (M.Sc.)”.

  • Within the AI program, the AI-FAPS project can be taken as “Project I” or “Project II” and is assigned to the pillar “AI Systems and Applications” due to its high application focus.
  • Within the Data Science program, the AI-FAPS project can be taken as part of the application subject “Material Science”.
  • Within the Computational Engineering program, the AI-FAPS project be can taken as a “Programming Project” in the 10 ECTS variant.

Notice: For master’s students in mechanical and industrial engineering, the institute offers separate projects, so-called project theses (12,5 ECTS incl. seminar), which are not to be confused with the present AI project module.

Learning outcomes

Students will

  • gain practical hands-on experience with an industrial AI use case,
  • learn to systematically develop and implement solution approaches,
  • familiarize themselves with suitable AI algorithms and implement them,
  • become proficient in using appropriate AI-related software libraries and frameworks, and
  • properly refactor and document their implemented code according to common conventions.

More information

Language: English or German, depending on the student’s choice

Current project topics as well as further organizational information can be found in the corresponding StudOn folder: https://www.studon.fau.de/cat4525463.html