This online program introduces the essentials of machine learning, focusing on its applications in natural science like data analysis and physical modeling. The course, centered on scikit-learn, combines theoretical insights with hands-on practice to provide the tools needed to get started with ML methods.
The 4-day online course will introduce you to machine learning. The potential applications of ML and deep learning methods to natural science research are numerous, including detector development, data analysis techniques, and even physical modelling. 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 and want to learn the basics of machine learning in a short block course. You may or may not join the intense study group afterwards to dive deeper into advanced topics. The introductory course will be based on scikit-learn and equip the participants with basic tools necessary to begin implementing and using ML methods. It will involve some theoretical descriptions but focus on hands-on exercises and discussions in the tutor group.
Learning goals
After this course, learners can embark on classification and regression projects using scikit-learn and should be able to transfer their knowledge to other platforms, such as TensorFlow and PyTorch.
Course date
Register now:
For more information on how to register, please follow the link on the course date.
Prerequisites
If you want to enroll in this course, we expect you to bring along knowledge of the Python language as taught in the courses “First steps in Python” and “Data processing with Pandas & Data visualization in Matplotlib” (basic Python, Pandas, Matplotlib).
Target group
This course targets researchers interested in machine learning.
This course is free of charge.
