In depth - MRes Machine Learning and Big Data in the Physical Sciences

Student handbook

Course overview

The MRes Machine Learning and Big Data in the Physical Sciences will cover the methodologies and specific toolkits related to research involving large data sets. The course will focus on the use of machine learning and data-science techniques in the acquisition, curation and analysis of extremely large datasets which are common-place in modern Physics research.

The challenges faced in Physics in particular, combined with both the very large datasets and data rates generated continue to make the field a unique development ground for machine learning and more generally artificial intelligence. 

The main component of this MRes is an extended (9 months) research project, starting in Term 2, where you will carry out original research embedded in a research group. You will have the opportunity to work on cutting-edge research topics, using machine learning and data science technologies to enhance that research. The project forms two thirds of the course, allowing you to fully engage with a research group within the Physics Department. You will have the opportunity to choose from a wide range of projects before being allocated during Term 1. 

In Term 1, you will take two core courses (see Module Specifications below), one in the theoretical aspects of data analysis, statistics and machine learning, and the other in the practical aspects of carrying out data analysis using commonly used packages. A personal laptop with the software needed for these taught modules installed will be provided for you. 

You will be assessed on various aspects of the course throughout the year. An indication of the timeline for these assessments can be found in this assessments timeline document.

Alongside these core aspects you will choose two FHEQ Level 6 or 7 elective modules from the Department of Physics courses list. It may also be possible to choose some electives from other departments. There is an elective that has been designed specifically for students on this MRes 'Accelerated processing for big data analysis', which you can find in the Module Specifications section below.

Course Directors

Module specifications

The main taught modules on this programme are the two core courses:

• PHYS70021: Statistical Methods for Experimental Physics
• PHYS70022: Practical Data analysis and Machine Learning in the Physical Sciences

There is also an elective module PHYS70071: Accelerated processing for Big Data analysis

Read more module specifications for the Department of Physics elective courses. 

Projects

The project module specification is PHYS70023: Research Project.

The list of available projects won't be published until the course starts, but you can view previous year's projects for reference.

Electives from outside the Department of Physics 

The following modules hosted by the Department of Mathematics are available as elective modules for MLBD MRes students:

• MATH70013: Advanced Simulation Methods (Spring Term, 5 ECTS)
• MATH70073: Advanced Bayesian Methods (Spring Term, 5 ECTS)
• MATH70079: Introduction to Statistical Finance (Spring Term, 5 ECTS)
• MATH70083: Statistical Genetics and Bioinformatics (Spring Term, 5 ECTS)

There are limited places available on these modules, and any module taken from outside of the Department of Physics must be first be agreed to by the relevant course directors.

Pre-course material

Take a look at our pre-course material, designed to help your prepare for starting the course.

Access the pre-course material.