April 2022 Machine Learning for Science Training Course Session Recordings

Dates: 4th April to 8th April 2022

The following links are only available to the attendees of the April 2022 training course. For details of upcoming courses, please follow the DiRAC twitter page.

Please follow the links below to view the recordings of the course lectures.

Classical Machine Learning

Introduction to Machine Learning

Supervised Learning Tequniques

Unsupervised Learning Techniques

Deep Neural Networks

Deep Neural Networks

Convolutional Neural Networks & Pretrained Models

Image Processing

Long Short-Term Memory (LSTM) Networks

Back Propagation and Gradient Descent

Debugging and Exploring ML Solutions

Generative Models

Generative Models Introduction

Generative Models Autoencoders

Generative Models VAEs

Generative Models GANs

Helping you prepare for the future


Dates: 4th April to 8th April 2022, Every Day 09.00 – 13.00

Day 1: Classical Machine Learning

09:00 – 09:30Introduction to Machine Learning
09:30 – 10:15Lecture – Supervised Learning Techniques
10:15 – 11:15Hands-On Practical
11:15 – 11:45Break
11:45 – 13:00Lecture – Unsupervised Learning Techniques

Day 2: Deep Neural Networks

09:00 – 09:45Lecture – Neural Networks
09:45 – 10:45Hands-On Practical
10:45 – 11:15 Break
11:15 – 12:00Neural Networks (Backprop)
12:00 – 13:00 Hands-On Practical

Day 3: Image Processing

09:00 – 09:45Lecture – Convoluted Neural Networks
09:45 – 10:45 Hands-On Practical
10:45 – 11:15  Break
11:15 – 12:00 Lecture – Autoencoder
12:00 – 13:00 Hands-On Practical

Day 4: Generative Models

09:00 – 09:45 Introduction to Generative Models & VAE
09:45 – 10:30 Hands-On Practical
10:30 – 11:00Break
11:00 – 11:45 Lecture – Generative Adversarial Networks (GANs)
11:45 – 12:30Hands-On Practical
12:30 – 13:00 Lecture – Debugging/ Exploring

Helping you prepare for the future

Machine Learning Techniques for Science

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.

Follow us on Twitter @DiRAC_HPC for future ML course announcements.

Helping you prepare for the future