29th November 2021 to 2nd December 2021, Every Day 09.00 – 13.00
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.
This course will help you to discover and learn how to apply machine learning techniques to scientific problems you face, and thus, will equip you with the skillsets you need to get creative in this rapidly growing field.
Description
The course provides 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, you will receive lectures, including worked examples to reinforce the concepts, and hands-on practical sessions. The practical sessions, where you will have access to dedicated GPU resources, are meant to ensure that you learn to apply your newly learned skills on practical problems stemming from physics, astronomy and other domains. The course will cover
- 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 is provided by DiRAC in collaboration with STFC’s Scientific Machine Learning Research Group (SciML) in the Scientific Computing Department. As such, the SciML team and the DiRAC RSE team will be available to guide you through the contents and hands-on sessions.
At the end of the course, you will take away the notes, worked examples, and a set of Jupyter Notebooks, which you can reuse in the future.
An Outline Timetable can be found here
Prerequisites
Basic programming skills in Python.
