Lecturer: Alexander König
Credits: 6 ECTS, 4 SWS
Dates: 22.04 ; 29.04 ; 06.05 ; 13.05 ; 20.05 ; 03.06. ; 10.06 ; 17.06 ; 24.06 ; 01.07 ; 08.07
Venue: Digital Bauhaus Lab

Description

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The aim of the course is to gain a critical understanding of machine learning and its application. The course focuses on the analysis of behavioral data and the prediction of trends and opinions, which form the core application of "AI". Another central topic is cloud infrastructures and the so-called "edge computing" or "Internet of Things", which together with machine learning form an almost all-encompassing set of tools for data collection that is beyond any (state) control. The course is therefore also suitable for those who are interested in a critical examination of "AI", but without wanting to delve further than necessary into the technology. The course gives an introduction to machine learning and its programming in Python using the Tensorflow libraries in a dedicated cloud infrastructure. Programming knowledge in Python is optional but not mandatory.


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Data Visualisations by Ryoji Ikeda (u.l.) and (u.r)Laurie Frick


We will use data from the social media platform reddit to perform common algorithms to extract behavioral data. With the help of machine learning systems that are trained on this Data, predictions of user behavior and altering trends in opinion are made possible.


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The seminar is not just a mere practical exercise but should enable students to enter the discussion about “AI” with profound critical and technical understanding. Therefore individuals with socio-cultural and political interests are highly welcome to enroll and contribute to this discussion.

• The results will be displayed on your own webpage. We simulate a modern cloud-based software-production workflow, virtual servers can be created in seconds through elaborate automation processes.

• We learn how to visualize such Data in other Video-Software like “Touchdesigner”, so you can build your own interactive environments.

Recommended Requirements

The students learn the software on the basis of their own projects, so the course is suitable for both beginners and advanced students.

Criteria for passing

In order to successfully participate, you will have to develop and document your own project. Also, complete the exercises and comply with the submission deadlines

Syllabus

===22. 04. 2022 | 11:00 to 14:30 |
===29. 04. 2022 | 11:00 to 14:30 |
===06. 05. 2022 | 11:00 to 14:30 |
===13. 05. 2022 | 11:00 to 14:30 |
===20. 05. 2022 | 11:00 to 14:30 |
===03. 06. 2022 | 11:00 to 14:30 |
===10. 06. 2022 | 11:00 to 14:30 |
===17. 06. 2022 | 11:00 to 14:30 |
===24. 06. 2022 | 11:00 to 14:30 |
===01. 07. 2022 | 11:00 to 14:30 |
===08. 07. 2022 | 11:00 to 14:30 |