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Artificial Intelligence for Innovation and Entrepreneurship

Module MGT001354

This Module is offered by Chair of Entrepreneurship (Prof. Patzelt).

This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.

Basic Information

MGT001354 is a semester module in English language at Master’s level which is offered in winter semester.

This Module is included in the following catalogues within the study programs in physics.

  • Catalogue of soft-skill courses
Total workloadContact hoursCredits (ECTS)
90 h 30 h 3 CP

Content, Learning Outcome and Preconditions


Artificial intelligence (AI) holds tremendous promise to benefit nearly all aspects of our society, including the economy, healthcare, security, the law, transportation, even technology itself. For organizations as well as for entrepreneurs there is no way around this technolgy, if they want to be and stay competitive. This module covers:
- Introduction to AI, algorithms, and machine learning
- The technology behind AI
- AI for innovation and entrepreneurship
- Ideating, assessing, prioritizing AI use cases
- Introduction to MLOps and building AI along the machine learning lifecycle
- Ethics and human centric design

Learning Outcome

Students gain understanding of the state of the art in artificial intelligence and how it is and can be applied in organizations and startups. Students will develop a solid and jargon free understanding of the technology and concepts such as AI, machine
learning and which opportunities and challenges it brings to organisations and society. Students gain the ability to ideate and assess their own AI use cases and learn what it takes to implement them bring them into production


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Courses, Learning and Teaching Methods and Literature

Courses and Schedule

SE 2 Artificial Intelligence for Innovation and Entrepreneurship (MGT001354) Post, T. Fri, 11:30–13:00, 1400
and singular or moved dates

Learning and Teaching Methods

The module is taught as a 2 SWS seminar. New concepts will be presented as lecture and then applied in group work in exercises which perpare students for the group presentation. To build bridges between course work and self-studying blended learning is applied.


Whiteboard, Slides, Code-Examples, Textbook, journal articles and papers


Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence.

Module Exam

Description of exams and course work

The module grade is based on a group presentation. During the seminar, students will ideate their own AI use cases, and assess them in terms of value and ease of implementation. In a group they will prioritize one use case and work on the implementation along the machine learning lifecycle taking into account ethical considerations. The group work has to be presented in the seminar and ends with a written report.

Exam Repetition

The exam may be repeated at the end of the semester.

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