Event

The module AI in Production is designed to teach students the practical, holistic integration of machine learning methods and the application of artificial intelligence in production. The course is oriented towards the phases of the CRISP-DM process with the aim of developing a deep understanding of the necessary steps and content-related aspects (methods) within the individual phases. In addition to teaching the practical aspects of integrating the most important machine learning methods, the focus here is primarily on the necessary steps for data generation and data preparation as well as the implementation and validation of the methods in an industrial environment.

The lecture "Seminar Application of Artificial Intelligence in Production" aims at the practical integration of current machine learning methods based on realistic industrial use cases. The content framework of the lecture results from the holistic, practical implementation of an AI project in production. First, the necessary Deep Learning programming basics are taught using the Keras software package. Subsequently, practice-relevant use cases are defined, which are to be implemented practically with the methods of machine learning and especially deep learning.

Learning Outcomes:

The Students

• are able to independently analyze a practical problem in production with regard to the application of machine learning methods.
• will be able to independently apply common deep learning algorithms to practical data sets, validate them, and analyze the results.
• understand the challenges of using deep learning methods in production.
• will know the main action areas and open research questions for the successful implementation of AI in production and for the implementation of autonomous machines.
• are able to evaluate the results of current deep learning methods and, based on these, to develop and practically apply proposed solutions (from the field of machine learning).
  
  

Workload:

regular attendance: 21 hours
self-study: 99 hours