|Dozent:||Klaus Diepold, Michael Zwick|
|Assistenten:||Matthias Kissel, Sven Gronauer|
|Turnus:||Sommersemester + Wintersemester|
|Zeit & Ort:|
The topic of the Machine Intelligence Seminar for the summer term 2020 is Causality in Machine Learning. We are going to explore how causality can be defined in a mathematical rigorous way, research about state of the art applications and use-cases, and discuss the meaning and importance of searching for causality in data science applications. The aim of the seminar is to gain an understanding of the current research on causality in data science applications in general, and to project the current trends into the future.
The seminar is structured into three phases: In the first phase, the students will read the seminal book "Elements of Causal Inference" from Peters, Hanzing and Schölkopf and get used to scientific work. The aim of the second phase is to summarize the state of the art of a specific research area to date. In this phase, students perform literature research together in groups and present their findings to the other students. In the last phase, the current trends and research directions should be projected into the future. As a result of this phase, each student group writes a book chapter on the future research development of a chosen research topic. These book chapters will be collected and published as a trend report after the seminar.