Machine Learning and Pattern Recognition


Lecturers: Chris Williams and Iain Murray
Location: Tuesday and Friday 10:00 - 10:50, Appleton Tower LT1
Office Hours: CW will be available Tues 9-9.50 in Appleton Tower concourse (weeks 2-7, 9 and 10).
IM will be available Weds 22, 29 Oct and 12, 19 Nov, 12:10-1pm, IF 1.46.
IM can also discuss things in the concourse on most Fridays after class.

Welcome! This course examines the principles, methods and application of machine learning: the business of utilising data to enable computers to perform tasks that would be infeasible to do well by direct programming. The course aims to provide a firm grounded understanding of the field and a knowledge of the methods that can be used to design machine learning tools. Alongside these fundamentals, we will teach about the theory and practice of a core set of machine learning tools, and students will gain practical experience using these tools. Please see this handout for more detailed information.

Preparatory Work

Students with little mathematical background would be well advised to do some preparatory work before the course begins.


Some of you may struggle with the mathematics in this course. Here is more information about the mathematical background assumed. Please work through Tutorial: Background Work to check your background.

Discussion Forum

We will be using Nota Bene as the discussion forum for this course. You can sign up for the forum here. Please use your UoE email address and your real name. The TA and the lecturers will be monitoring the site regularly. You are urged to use the discussion site to ask any questions about the course.

To encourage you to use the site, we will have several special policies:

  1. By default, we will respond to all questions we get over email about the course content by posting the question and answer anonymously onto the discussion site. If your question is of a clearly confidential nature, obviously we will not post it to the discussion forum! Additionally if you prefer that we answer your question privately, please mention this in your email and we will honour your request.
  2. The lecture notes of the course will be made available only through NB. You are welcome to download them from NB to your own computer as PDFs. For example, you might do this if you prefer to revise offline or on a mobile device.
  3. NB allows you to click on any of the slides and leave a short comment, e.g., "Could you explain this slide again?" We will monitor all of the topics that students have asked me to explain again, and every week will pick one question to answer in class. So you can ask us to explain slide 33 again, and I will! I will aim to do this first thing on Tuesday's lecture, every week from Week 3 onwards.

Although NB supports Firefox, Chrome, Safari, and IE9, some students have reported problems this year with certain browsers. If you are having technical trouble, try logging in with Chrome. Bugs can be reported on the NB discussion forum. In my experience the NB team are very responsive.


The course text will be Machine Learning: A Probabilistic Perspective. Kevin P Murphy, MIT Press, 2012.

A similar recent book, which is also excellent, is Bayesian Reasoning and Machine Learning. by David Barber, 2012. It is available as a free pdf online.

Either one of the above two books is sufficent to do well in this course. For those who want to read even more, I'd also suggest:

David MacKay's book, "Information Theory, Inference and Learning Algorithms" is a classic book that gives a different perspective on many of the methods in this course. It can be read online for free.

Finally, the book The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Hastie, Tibshirani, and Friedman. (PDF available online.) is a perspective on machine learning by several leading statisticians. It is very much complementary to the course texts.

These pages are maintained by Chris Williams and Iain Murray.

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