STFC School on Data Intensive Science 2020

Session

Parallel Session ML/AI

12 Oct 2020, 10:30

Zoom Meeting

Description

During the parallel sessions participants will break into smaller groups for focused discussions on selected topics.

7. HEP NN training

Gregor Ksieczka

12/10/2020, 10:30

This session will introduce aspects of unsupervised (and weakly supervised) learning methods and demonstrate these concepts using concrete problems from particle physics. The availability of high-quality synthetic data from Monte Carlo (MC) simulation is a key ingredient for the success of particle physics. However, the production and storage of these MC simulations occupies a large fraction...

6. Introduction to ML

Meirin Oan Evans

12/10/2020, 10:30

This session is an introduction into machine learning. Machine learning is everywhere in modern “big-data” science. As physicists and big-data scientists, it’s a good idea to know a bit about machine learning. The aim of this module is to explore what it means to build a machine learning model and expand on concepts in machine learning that are essential to anyone working in big-data...

8. Scikit/Keras

Adrian Bevan

12/10/2020, 10:30

We will explore practical applications of TensorFlow 2.0 using Keras to build models. The aim of these tutorials is for you to learn how to construct models to work with different shape feature spaces (both image data and flat input data) with several different types of neural network and to explore common issues that can be encountered when applying training to data.

10. HEP NN training

Gregor Ksieczka , Lisa Benato

12/10/2020, 13:30

This session will introduce aspects of unsupervised (and weakly supervised) learning methods and demonstrate these concepts using concrete problems from particle physics. The availability of high-quality synthetic data from Monte Carlo (MC) simulation is a key ingredient for the success of particle physics. However, the production and storage of these MC simulations occupies a large fraction...

9. Introduction to ML

Meirin Oan Evans

12/10/2020, 13:30

This session is an introduction into machine learning. Machine learning is everywhere in modern “big-data” science. As physicists and big-data scientists, it’s a good idea to know a bit about machine learning. The aim of this module is to explore what it means to build a machine learning model and expand on concepts in machine learning that are essential to anyone working in big-data...

11. Scikit/Keras

Adrian Bevan

12/10/2020, 13:30

We will explore practical applications of TensorFlow 2.0 using Keras to build models. The aim of these tutorials is for you to learn how to construct models to work with different shape feature spaces (both image data and flat input data) with several different types of neural network and to explore common issues that can be encountered when applying training to data.