Teaching
I really enjoy teaching. It is one of the best ways to overcome the narrow and sometimes overly specialized perspectives that research can produce. For me, teaching is about placing research topics into context, developing intuitive conceptual structures, and discussing ideas together with students. In recent years, I have taught several courses for students in philosophy, statistics, cognitive science, and computer science:
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What to Learn from the Impossible? Formal and philosophical
topics of impossibility theorems,
summer term 2024/25, philosophy, cognitive science, and computer science departments,
syllabus. -
Maths Primer for Mathematical Philosophy Students,
winter terms 2024/25 and 2025/26, philosophy department,
script. -
Explainable Artificial Intelligence,
winter term 2021/22, philosophy and statistics departments,
syllabus. -
Causality and Machine Learning,
summer term 2021, statistics department,
syllabus. -
Philosophy of Artificial Intelligence,
winter term 2020/21, philosophy department,
syllabus. -
Ethics of Artificial Intelligence,
summer term 2020, statistics department,
syllabus.
I also helped design the lecture Interpretable Machine Learning at the LMU statistics department. In addition, I have assisted in the following courses:
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Models and Simulations,
summer term 2020, philosophy department,
syllabus. -
Central Topics in Philosophy of Science,
winter term 2019/20, philosophy department,
syllabus. -
Linear Algebra I & II, Analysis I & II,
2016–2019, mathematics department.
For several courses, I have created additional teaching materials, including a short introductory YouTube course on NetLogo, a formal script for the CTPS course, and a simple example for students who want to get started with LaTeX.