Unit information: Advanced Computational Physics and Machine Learning in 2026/27

Please note: Programme and unit information may change as the relevant academic field develops. We may also make changes to the structure of programmes and assessments to improve the student experience.

Unit name Advanced Computational Physics and Machine Learning
Unit code PHYS30053
Credit points 20
Level of study H/6
Teaching block(s) Teaching Block 4 (weeks 1-24)
Unit director Dr. Jim Brooke
Open unit status Not open
Units you must take before you take this one (pre-requisite units)

PHYS20040 From Classical to Modern Physics

One of:

  • PHYS10013 Practical Physics I: Laboratory Skills, Computing and Team Discovery
  • PHYS10014 Practical Physics I: Laboratory Skills and Computing
  • SCIF10002 Introduction to Coding and Data Analysis for Scientists

AND one of:

  • PHYS20035 Computational Physics and Data Science
  • SCIF20002 Programming and Data Analysis for Scientists
Units you must take alongside this one (co-requisite units)

-

Units you may not take alongside this one

SCIF30005 - Core Programming, Visualisation and Data Analysis for Scientists

School/department School of Physics
Faculty Faculty of Science

Unit Information

Why is this unit important?

This unit continues your study of computational physics, extending your learning from years 1 and 2. The unit will cover more advanced techniques in terms of both programming and numerical methods, as well as introducing machine learning. You will practise applying these methods in a range of physics contexts : modelling, simulation and data analysis. However, all methods covered have a very broad range of applications within and outside science, and are increasingly important across research and industry.

How does this unit fit into your programme of study?

This unit forms part of the third year options portfolio for physics students; a suite of options designed to explore the wider applications of physics as well as further depth in specific areas. Your choice of options will help to shape the physicist you will become.

Your learning on this unit

An overview of content

This unit will extend your understanding of numerical methods, to cover linear algebra and Monte-Carlo techniques. It will introduce advanced programming techniques such as obect-oriented programming. A significant component of the unit will introduced management of large datasets and their analysis via machinea learning approaches.

Topic areas will include:

  • Advanced python programming and object-oriented approaches
  • Applications of linear algebra in computational problems
  • Monte Carlo simulation
  • Introduction to machine learning for regression and classification
  • Handling and processing large datasets
  • Introduction to high-performance computing

How will students, personally, be different as a result of the unit

By the end of this unit, you will become adept at the application of computation in solving physics problems, as well as identifying strategies to solve problems outside the Physics context.

Learning outcomes

By the end of this unit, you should be able to:

  • Develop and apply code to solve problems in Physics
  • Apply your physics knowledge across topic boundaries and in unrehearsed contexts
  • Use numerical methods to model, describe and predict physical outcomes
  • Extract information from large datasets through machine-learning techniques

How you will learn

The unit is organised through our on-line learning environment (OLE). This is where you will find information about the unit, lecture notes, any pre-recorded videos, recordings of lectures and live sessions, access to online quizzes (where appropriate) and other learning resources.

All teaching activities will be delivered face-to-face (barring intervention from exceptional events), and it is an expectation that you engage with these activities. Learning activities will be split across in-class activities (lectures, problems classes) and those around your own private study (for example online quizzes, videos, textbook references etc.).

The unit will consist of around 20 hours of content delivery with 40 hours of computing workshops. Along with this time there is an expectation of personal study in line with the University statement on student workloads.

Some sessions may require preparation beforehand (e.g. watching a video, reading a textbook chapter or journal article or similar); where these materials are provided, you should aim to spend around one hour of preparation time for one hour of face-to-face teaching. This will allow you to make the most of class discussions and activities.

Computing workshops will be conducted in a range of group sizes and all will have emphasis on problem-based learning, where you will be able to discuss the problems with others in your group.

We will make use of online quiz tools to allow you to practice problems and get rapid feedback on your solutions. You will build understanding attempting problems, gaining feedback on previous attempts and being able to use this feedback to attempt the problem again. These formative exercises will not contribute towards your grade for the unit.

How you will be assessed

Tasks which help you learn and prepare you for summative tasks (formative):

There will be regular formative online quizzes available through the online learning environment (OLE) to generate rapid feedback on your understanding. There will also be regular computing workshops, allowing you to ask questions of the facilitator to help you quantify your own understanding and that of others, and to gain verbal feedback on your problem solving skills.

Tasks which count towards your unit mark (summative):

  • Coursework 1: A computer-based test to demonstrate understanding of numerical methods and programming principles in the context of the advanced physics content of the unit (30%, ILOs 1,2,3)
  • Coursework 2: An extended exercise to develop code to solve a specific problem in simulation, modelling or data analysis (70%, all ILOs).

When assessment does not go to plan

If you do not pass the coursework assessment, you may have the opportunity to retake a single coursework assignment in the coursework reassessment period. *

  • subject to passing a minimum overall number of credits for the year.

Resources

If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. PHYS30053).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the University Workload statement relating to this unit for more information.

Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.

The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates within the Regulations and Code of Practice for Taught Programmes.