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.