4th April 2022 to 8th April 2022
Course: Machine Learning Techniques for Science
Science is undergoing a data explosion and the advent of Artificial Intelligence and Machine Learning techniques is revolutionizing the way scientists tackle their research. Simulations and observations now generate Petabytes of data and machine learning is providing novel and powerful methods for analysing those experimental datasets and extracting essential science in ways that have not been possible before.
The course provided a practical, and hands-on introduction to the concepts, methods, and toolkits for applying machine learning to fundamental scientific problems. Held virtually over five consecutive mornings, lectures, including worked examples to reinforce the concepts, and hands-on practical sessions were delivered. The practical sessions, where participants had access to dedicated GPU resources, were meant to ensure that participants learned to apply their newly learned skills on practical problems stemming from physics, astronomy and other domains. The course covered.
- Conventional machine learning techniques (such as decision trees),
- Neural and deep neural networks, the cornerstone of modern AI
- Generative models to enable you to generate synthetic, yet realistic, datasets with labels, and
- Debugging and RSE aspects of the machine learning
The course was provided by DiRAC in collaboration with STFC’s Scientific Machine Learning Research Group (SciML) in the Scientific Computing Department.
An Outline Timetable can be found here
Basic programming skills in Python.
Previous Course Testimonials
“I found the course to be the ideal mix between theory and practical examples. Very informative on how to use ML for cutting-edge science research questions.”
“This course was the perfect introduction to machine learning from a scientific perspective; instructors were enthusiastic and very helpful, and the lectures and materials were approachable for someone with no previous experience, while remaining detailed enough to be of practical use. I felt I gained a good overview of a range of important techniques and algorithms and will certainly apply the knowledge gained during my chemistry PhD.”
Places on the Machine Learning Techniques for Science course are allocated giving priority to individuals who are part of a DiRAC project. If you would like find out how to become a DiRAC user, please see our latest call for proposals.
Registration is now closed.
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