Tutoring and supervising
I currently personal tutor for second-year students. This covers a range of subjects from classical mechanics through electromagnetism to quantum mechanics and condensed matter. The reading list is available from the University Library. Tutors are also responsible for teaching various key skills, such as essay writing. Further support is available through the Academic Skills Centre who run mathematics drop-in sessions amongst other things. To brush up on your writing, a handy reference has been compiled by Trevor Ponman, and international students might be interested in English language support provided by the University.
Last academic year I co-supervised fourth-year projects for MSci Physics and Astrophysics students, and third-year group studies projects. Both provide an opportunity for undergraduates to develop research skills, practise their writing, and learn about astrophysics.
Information theory for physicists
I have given a short lecture course on information theory aimed at advanced undergraduate and postgraduate students. The course is self-contained such that it doesn’t require any prior knowledge. We covered some areas of information theory most applicable in physics:
- Lecture 1 — Probabilities, inference and information content
- Lecture 2 — Entropy and probability distances
- Lecture 3 — Maximising entropy and thermodynamics
I made use of David MacKay’s excellent Information Theory, Inference, and Learning Algorithms throughout. You can also watch his lectures on Information Theory, Pattern Recognition and Neural Networks online.
The course does not cover coding theory in much depth, but this is discussed (in detail) in the papers of Claude Shannon which founded the field, A Mathematical Theory of Communication: Parts I & II and Parts III–V. Sections 6 and 7 of Part I are particularly useful if you want to know more about information entropy.
If you would like to go beyond the course to learn more about information geometry, I recommend Methods of Information Geometry by Shun-ichi Amari and Hiroshi Nagaoka.
Tutorials in statistical inference
In 2015, I helped our graduate students to organise a series of workshops for researchers to learn about inference. We brought together experts from a range of fields (from astrostatistics to epidemiology) and covering a range of topics, from the basics of Bayes’ theorem through to sophisticated techniques such as Hamiltonian Monte Carlo and hierarchical modelling. I learnt something too!
Estimating quantities is a useful skill for physicists. I have compiled a Guide to Estimation which may come in handy.
To pick up some more useful tools for estimation, I would recommend Street-Fighting Mathematics by Sanjoy Mahajan.