General Information
Module Assessment
- The assessment for this lecture consists of working on a case study.
- The case study is completed as group work in teams.
- The work period spans the entire semester.
- For grading, you must submit a prototype of an AI agent .
- The results must be submitted at the end of the semester via Teams.
Tutorial
For working on the case study, you may make use of support from our tutor. Communication generally takes place via Teams. Details will be announced during the lecture.
Software
- In this lecture, we use the programming language Python and the development environment JupyterHub.
- It is strongly recommended to use this environment, but you may also install Python and Jupyter locally on your own infrastructure.
Self-Learning Platform: DataCamp
You have the opportunity to expand your methodological knowledge and your practical skills in Python (or other analytical tools) with the self-learning platform DataCamp.
To get free access to all DataCamp courses, register with your FH Münster email address via this link:
Join DataCamp Classroom.
ILIAS
ILIAS will not be actively used in this lecture. All materials, announcements, and submissions are handled via Teams.
MS Teams
- For collaboration during the lecture and the case study we use Microsoft Teams.
- Questions in Teams are answered by the tutors and by me.
- Please use the public channels to ask questions so other teams can benefit from the answers as well.
- For larger issues, personal appointments are also possible.
- Questions in Teams are answered by the tutors and by me.
- During the sessions, we will jointly develop the necessary toolkit (e.g., fundamentals and methods of Generative AI, implementation using Python).
- For each topic block, we will conduct hands-on exercises using example datasets.
- We will also discuss frequently asked questions together during class.