Team

• Matthias Aßenmacher is a Postdoc at the Chair for Statistical Learning and Data Science at LMU. He extended Ben’s initial course by creating most of the material on Transfer Learning, BERT, and beyond. He is responsible for restructuring and extending the course, maintaining the website, and organizing the course at LMU.
• Christian Heumann is a Professor at the Department of Statistics at LMU. He is responsible for teaching the machine learning basics.
• Benjamin Roth is a Professor at the Faculty of Computer Science at the University of Vienna. He created the initial version of this course back in 2019.
• Hinrich Schütze is a Professor at the Center for Information and Language Processing at LMU. He is responsible for most of the content on LLMs.
• Andreas Stephan is a PhD student at Ben’s group who joined the team in 2022 and contributed to restructuring and extending the course.
• Leonie Weißweiler is a PhD student at the Center for Information and Language Processing at LMU. She created the exercise sheets for the course. She is responsible for restructuring and extending the course as well as for the organization of the course at LMU.
• Michael Sawitzki is a LMU Master student in Statistics & Data Science and joined the team in summer 2024. He created the chapter on Decoding and Transformer parameter counts and was responsible for restructuring the website.

Alumni

• Ingo Ziegler and Marwin Härttrich (both 2023 – 2024) contributed by creating the material for the programming assignments.
• Erion Çano (2022 – 2024) contributed to restructuring and extending the course.
• Nina Poerner and Marina Speranskaya (before 2020) contributed to the course material up until the transformer, back in the days when Ben still taught this course at LMU.

Contributors welcome

If you want to contribute to this effort, please consider joining the team and email us now:

• LMU: matthias@stat.uni-muenchen.de
• Univerity of Vienna: andreas.stephan@univie.ac.at

Our contributing guidelines may be found here.

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

We are developing the course on GitHub.

We would appreciate if you contact us in case you are re-using our course. Knowing this helps us to keep the project alive. Thank you!