# Teaching by Iain Murray

**Notes on core skills:** writing, speaking.

See also: Simon Peyton-Jones’s notes; The Researcher’s Bible and other How to Guides by Alan Bundy. Ali Eslami’s notes on organizing machine learning code.

**Mathematics summary sheet:** a check-list of background knowledge, originally for Zoubin
Ghahramani’s 2003 Machine Learning course. Feel free to use and adapt:
[PDF, 2up-pdf,
LaTeX Source].

You may also be interested in the notes on my publications page.

**Undergraduate and master’s projects**.

Thanks to the generous comments of the class of 2014–15, I was awarded the 2015 Van Heyningen Award for Teaching in Science and Engineering. I was also the runner-up for this award in 2019–20.

## Lectures

Iain Murray’s talks and lectures on videolectures.net.

Reverse chronological list of lectures:

- MLSS Africa 2019 (site, videos).
- MLPR, a machine learning course in Edinburgh.
- Outline notes from Astro Hack Week 2018.
- Monte Carlo tutorial, at Neural Information Processing Systems 2015.
- Machine Learning Summer School, Iceland, 2014.
- Inf2b Learning and Data, Edinburgh UG2 course.
- Information Theory, Edinburgh MSc course.
- MCMC for deep learning and density estimation, IPAM Graduate Summer School 2012.
- IPAM Graduate Summer School 2011, Probabilistic Models of Cognition: official site, my slides and additional pointers.
- Guest lecture in
*Introduction to Research Computing*: Efficient Matlab/Octave. - Information Theory, Edinburgh MSc course.

As first taught in 2010. - I gave an introduction to Machine Learning at the PASCAL bootcamp 2010. This lecture reviews some simple classification and regression rules, discusses under- and over-fitting and emphasises the utility of defining objective functions for learning. There is also a short overview of Bayesian learning, and some practical tips for pre-processing and visualizing data. The lecture ends with a brief mention of unsupervised learning and related topics.
- I gave a Markov chain Monte Carlo tutorial at the machine learning summer school in Cambridge in 2009. My lecture slides and practical notes are available.
- I gave a double lecture on Gaussian processes for CSC2515, Introduction to Machine Learning, at the University of Toronto in 2008. Slides as PDF.
- I gave a lecture on clustering for CSC384H Introduction to AI, at the University of Toronto in 2008.
- I gave a double lecture on Monte Carlo and MCMC for CSC2535 Advanced Machine Learning, at the University of Toronto in 2008.
- I gave a lecture on MCMC in the Cambridge engineering department’s Machine Learning 2006 Advanced Tutorial Lecture Series.
- I also taught a couple of lectures on Monte Carlo for 4F13 Machine Learning at the University of Cambridge in 2006.
- I gave a general-audience lecture on AI to the
*Thinking Society*at Cambridge University in 2006. Amongst other things, I talked about Oranges and Lemons. - At UCL I was involved in some teaching for Supervised Learning 2004. Slides, demos and course work are on the official site.
- I was a TA for and created worked answers for Zoubin’s Unsupervised Learning 2003 course at UCL.