The main goal is to give students an introductory application-specific knowledge about artificial intelligence and data analytics in the engineering domain. More specifically, an introduction will be given for the topics of data preparation, data integration, data presentation, supervised learning, unsupervised learning, reinforcement learning, and neural networks.

The lecture and the content of the lecture are all in English.

The course is composed by two weekly appointments, through 12 weeks. The first one is a theoretical lecture and the second one is a hands-on exercise session. The hands-on session follows the previous lecture topics to enhance the understanding of the presented theory via hands on use cases.

There are no official prerequisites to register for the course. Yet, the course is heavily dependent on programing with Python. Although the basics of Python will be introduced but a general knowledge in programming in general and OOP is expected. Otherwise, students have to work on improving their programming knowledge during the semester.

The lecture sessions will have a total load of 2 SWS, similarly the exercises will have a load of 2 SWS. In addition to attending the lectures and exercises, it is expected a weekly self-study of roughly 90 minutes

AIDAE lectures and exercises are offered only in the summer semester. Nevertheless, a repetition examination is also offered in the winter semester.