IE 694: Industrial Applications of Artificial Intelligence

Organization

Lecturer: Prof. Dr. Heiner Stuckenschmidt
Assistants: Alexander Beier, Lea Cohausz, Patrick Betz, Jakob Kappenberger, Keyvan Amiri Elyasi
Contact: ralph.alexander.beiermail-uni-mannheim.de
Type of course: Lectures, exercises (6 ECTS points)
Examination: Students create a “Learning Portfolio”, which consists of four separate submissions spread out through the semester, one for each industrial sector. Students are required to hand in two theoretical and two practical submissions. A theoretical submission is a short scientific article, such as the discussion of a published paper, a literature review etc. A practical submission is an implementation of an industrial application of artificial intelligence in Python (which requires prior knowledge in machine learning and Python, see recommended knowledge). More details and examples are provided in the first lecture.

Admission (FSS 2024)

IMPORTANT: The number of participants is limited. To apply, send an up-to-date transcript of records to ralph.alexander.beiermail-uni-mannheim.de by February 6th. Transcripts should be in English or German. If your grading system differs from the German one, please attach a description. Additionally, you have to register in portal2 as usual. We will let you know if you are admitted to the course on February 7th.

Content

Participants will learn about the use of Artificial Intelligence methods, mostly from the field of machine learning in different sectors and industries. They will learn about application areas in the primary, secondary and tertiary sector, get an introduction to examples of such applications that have been published on a scientific level and gather some experience in working with data from the respective fields using publically available datasets.

Learning outcomes / expertise

Expertise:

Students will acquire knowledge about possible applications of machine learning in different branches of industry as well as the dominant methods used in these areas:

Primary Sector: Agriculture, Energy Production
Secondary Sector I: Construction, Manufacturing
Secondary Sector II: Supply Chain Management, Business Process Management
Tertiary Sector: Healthcare, Education, Finance

Methodological competence:

Successful participants will be able to: Identify potential for applying AI methods in different areas of industry; Decide on a suitable method for addressing typical problems in these industries

Personal competence:

Participants will learn to reflect and document their own learning process

Recommended Knowledge

Machine Learning Concepts and Techniques
Programming in Python