This online course explores the basics of deep learning and its use in natural science, from data analysis to detector design. Aimed at those with prior Python and machine learning experience, it emphasizes hands-on practice with PyTorch, enabling participants to start working with deep learning techniques.
This 3-day online course will introduce you to machine learning. The potential applications of 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 have been introduced to machine learning. Enroll if you want to learn the basics of deep learning in a short block course. The introductory course will be based on pytorch and equip the participants with basic tools necessary to begin implementing and using DL 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 pytorch and should be able to transfer their knowledge to other platforms, such as TensorFlow or Keras.
Course date
Register now:
September 08–10, 2025
December 15–17, 2025
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). We also assume that you have acquired the content of “Introduction to Machine Learning” or “Machine Learning 1”.
Target group
This course targets researchers interested in machine and deep learning.
This course is free of charge.
