On April 16th (8:00pm GMT+2) we will be delightful to host Heidi Seibold from University of Munich (Department of Statistics joins LMU Open Science Center)
). Heidi will share her experiences on Teaching Machine Learning online – very popular topic in the current situation of COVID-19. The abstract and the biogram are below.
See you on the Webinar!
- channel: youtube.com/c/WhyRFoundation
- date: every Thursday 8:00 pm GMT+2
- format: one 45 minutes long talk streamed on YouTube + 10 minutes for Q&A
- comments: ask questions on YouTube live chat
The course “Introduction to Machine Learning” is designed as a flipped classroom course. That means the lecture part of the course is presented online with videos, online quizzes and coding exercises while the in-person classes are spent answering questions and solving and discussing exercises in an interactive fashion.
Due to the COVID-19 crisis, we were not able to teach the class in person this year but had to go virtual. In this talk I will discuss how we successfully moved online and how you can contribute and copy what we did using our openly licensed course material (see https://compstat-lmu.github.io/lectur…
Heidi Seibold is a medical AI researcher, open science advocate and research software engineer. She believes that good research is reproducible, reusable and open and spends most of her time trying to improve the way we do research. She teaches machine learning, R, and open and reproducible research.
Heidi studied statistics at LMU Munich and did her PhD in computational Biostatistics at the University of Zurich. She worked as lead of the DIFUTURE analysis group, as deputy professor of biostatistics at LMU, and is currently affiliated with the Munich Center of Machine Learning (LMU Munich), the University of Bielefeld, and the Helmholtz Zentrum München. Heidi is a chair of useR! 2020 European Hub in Munich (https://user2020muc.r-project.org/).