Course Catalogue

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This course will include further learning about computational physics at a level that is accessible to Physics Msci students, which will include reading and computational exercises on basic topics (libraries, Git, multivariate fitting, machine learning and AI basics) followed by a longer piece of project work applying some of the techniques to suitable datasets and problems.

6 hours nominal learning per week in Semester 2

Week 22: introductory lecture giving overview of the course and a brief outline of the content of the labs in Weeks 6-8

Weeks 22-24: one 3 hour lab session provided one afternoon a week with teaching staff available to discuss exercises and the project work - students are expected to do more work in their own time

Weeks 25-32: independent work on a longer project-style assignment, with the 3 hour lab session available so students can work there or drop in and ask questions. The project work will have a small number of different projects supervised by different staff members, with each being taken by a group of students, who will meet with the relevant supervisor in the lab on a regular basis, but will ultimately be responsible for completing their assignment independently.

Interim talks in week 30 with questions (assessed)

Final report due in last week of teaching in Semester 2 (week 32)

PHYS4009 Honours Physics Laboratory

PHYS4008 Honours Computational Physics Laboratory and PHYS4029P Theoretical Physics Group Project.

The initial 3 weeks will have hand-ins of laboratory records of their work on the teaching laboratory components in the form of answers to well-defined questions and challenges (5% for each). Formative feedback will be given.

The longer piece of project-style work will be assessed by supervisor assessment of progress (30%) (including assessment of the code base and commit history in git), teaching staff assessment of a talk halfway through (20%) and an assessment of the final report (35%).

Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

To train MSci Physics students in the use of more advanced computing (such as code libraries, complex fitting and machine learning) from an applied perspective in the form of a few assessesd lab exercises accompanied by four lectures outlining some key concepts and tools, followed by their application in a project assessed on the basis of project performance as observed by one of the teachers/demonstrators (with assistance of looking at the GIT commit history), an interim talk to the teachers/demonstrators and their cohort, and a final written formal report.

By the end of this course students will be able to:

■ Use 3rd-party code libraries and understand how to use the documentation within to guide usage

■ Understand how to maintain their own codebase in a git repository

■ Fit complex functions of multiple variables to data using computational tools

■ Appreciate the essential principles of machine learning and some of the main classes and approaches

■ Have a basic understanding of generative AI and LLMs

■ Use some of these skills in performing a longer piece of project-style work on real physics datasets

■ Reinforce previously learned skills on report writing and oral presentation

■ Improve their abilities in the graphical presentation of data using common python libraries

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.