Programme

Dates: 1st March 2021 to 5th March 2021, 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

1st March 2021 to 4th March 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.

Registration

Registration has now closed. We will let successful applicants know by the 16th February.

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

Helping you prepare for the future