SS 2021: Machine learning for the biomolecular world

By Frauke Gräter and Rebecca Wade.

Over the last decade machine learning revolutionized computer vision and language processing. This progress was fueled by the development of new methods as well as the availability of ever powerful hardware. Now a new wave of research adapts these advances to increase our understanding of the molecular world. In this seminar we will explore the recent literature on these efforts ranging from protein structure and dynamics to drug design. After the high interest in this literature seminar in 2018, with at that time a more general focus on molecular properties, we will in this year focus on recent progress on biomolecules.

The seminar is targeted toward advanced Bachelor, Master and interested PhD students. As the seminar’s topics cover a broad range, we are happy to welcome students from all scientific backgrounds with a strong interest in interdisciplinary work, preferably with some background in machine learning, and/or biomolecular simulations.

Time: Thursdays 2.30- 4pm pm starting April 15 2021
Place: Online over zoom.

In our Vorbesprechung/briefing, we will distribute topics and dates. The first ~2 seminars will be used to introduce some basics of machine learning and relevant molecular simulation techniques.

Registration:

Only a limited number of students can be accepted (24 students max). Please register in advance by sending an Email to Dr. Barbara Port (barbara.port@h-its.org)

We will use moodle to exchange course material and information including the zoom link. Please subscribe to the course on moodle (Heidelberg University).

Credit points:

• master physics: 6 CP
• master molecular biotechnology and other masters in biology: 4 CP

Recommended reading:

• Andrew R. Leach, “Molecular Modeling: Principles and Applications”
• Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, “An Introduction to Statistical Learning” http://www-bcf.usc.edu/~gareth/ISL/
• Ian Goodfellow and Yoshua Bengio and Aaron Courville, “Deep Learning Book” http://www.deeplearningbook.org/