In this online course, participants will be introduced to uncertainty quantification methods and their significance in enhancing scientific research. Designed for participants with Python skills and basic statistical knowledge, the course uses PyTorch to provide practical tools and hands-on experience with UQ techniques.
This half-day online course will introduce you to methods of uncertainty quantification (UQ) in machine learning. The potential applications of UQ methods to natural science research are numerous and can not be understated for their importance in the scientific process. These techniques will be essential to ensure the highest-quality and impactful scientific results from existing and future experimental works.
The basic course is for you if you do know Python, have basic statistical experience and know how to train a model with pytorch. The introductory course will be based on pytorch and equip the participants with basic tools necessary to begin implementing and using UQ methods. It will involve some theoretical descriptions but focus on hands-on exercises and discussions in the tutor groups.
Learning goals
After this course, learners can embark on using UQ methods pytorch and the uncertainty toolbox.
Course date
Register now: October 02, 2025
For more information on how to register, please follow the link on the course date.
Prerequisites
To participate in this course, you need to be actively working on the development of AI models.
Target group
This course mainly targets Helmholtz researchers who work on developing AI models and seek for efficient strategies to think about and report ethical issues.
This course is free of charge.
