F11RC - AGQ: Research Computing

Course leader(s):

Aims

This course will introduce students to an advance computing methodology, with a mix of lectures and hands-on working.

Topics will come from a rota, including:

Programming mathematics in Lean (automated theorem-proving software)
Topological data analysis
Machine learning in topology and in QFT

Computer algebra systems such as Sage, Mathematica, Maple, MatLab

Syllabus

1.1 Research computing will focus on machine learning, surveying applications to quantum collider physics and to topological structures in biology, and on topological data analysis

2.1 Further topics may include mathematical computing for problems in algebra, geometry, number theory, etc, quantum field theory techniques in machine learning, as well as interactive theorem proving with Lean.

Learning outcomes

By the end of the course, students should be able to do the following:

obtain fluency with cutting-edge research-level computing software
practice problem-solving, modelling, and data analysis skills.

Further details

Curriculum explorer: Click here

SCQF Level: 11

Credits: 15