On the theme of maths teaching and outreach, here are some projects that I’ve worked on previously that are public and free to use. For my writing check the blog. For specific details on teaching experience, check on my work profile instead.

Decision Tree Explorable

Spamfilter, Peter Eich (2006)

In the summers of 2020 and 2021 I delivered a mini-course as part of the Sutton Trust summer school on machine learning alongside Isabella Deutsch. My primary contributions were an R/Shiny app (code, app) for exploring an application of decision trees and a series of mini research projects on that and applied maths more broadly.

Visualisation Workshop

Diagram of the causes of mortality in the army in the East, Florence Nightingale (1858)

Materials for a workshop created for a collaboration between ICMS and Fife College as part of their prison education programme. It aims to help learners understand common visualisation types, draw conclusions based on represented data, and and how to identify and critically analyse misleading visualisations.

Access Mathematics

Pissarra Matemàtiques 2, Pau Colominas (2019)

Within Centre for Open Learning, I’ve been creating a quarto book that covers content from National 5 and Higher maths that relevant for arts, humanities, and social science students. These are being written following current best practice for creating mathematics notes, and we aim to release them as an Open.Ed resource; watch this space!

Sparse Regression

Lasso Regression, Fate Mousavi (2022)

These slides explain Lasso regression at an undergraduate statistics level, originally created for a job interview task. While I plan to turn them into a general resource, in the meantime the GitHub repository serves as a nice demo of how to create maths presentations that are more accessible using reveal.js and quarto.

Computation Notebooks

Python Code, Thraea19 (2020)

Nonlinear Optimization is a masters level course at Edinburgh. Its biweekly computing labs make use of Python scripts, but coding knowledge isn’t a prerequisite so not every student is familiar. To help make this more accessible, I worked with the course organiser to port the Python scripts to Jupyter notebooks hosted on Noteable. The source code for these can be found on GitHub.