

Department of Engineering  
Taught By: Professor Carl Edward Rasmussen
Code and Term: 4F13 Michaelmas term
Year: 4th year (part IIB) Engineering and MPhil in Machine Learning and Speech Technology; also open to MPhil and PhD students in any Department.
Structure & Assessment:14 lectures, 2 coursework revisions, 3 pieces of course work. The evaluation is by coursework only, all three pieces of course work carry an equal weight. There is no final exam.
Time: 8 double lectures on Fridays at 14:00  16:00.
Location: Lecture Theatre 2 (LT2), Inglis Building, Department of Engineering, Trumpington Street (map).
Prerequisites: A good background in statistics, calculus, linear algebra, and computer science. 3F3 Signal and Pattern Processing. You should thoroughly review the maths in the following cribsheet [pdf] [ps] before the start of the course. The following Matrix Cookbook is also a useful resource. If you want to do the optional coursework you need to know Matlab or Octave, or be willing to learn it on your own. Any student or researcher at Cambridge meeting these requirements is welcome to attend the lectures. Students wishing to take it for credit should consult with the course lecturers.
Textbook: There is no required textbook. However, the material covered is treated excellent recent text books:
Kevin P. Murphy Machine Learning: a Probabilistic Perspective, the MIT Press (2012).
David Barber Bayesian Reasoning and Machine Learning, Cambridge University Press (2012), avaiable freely on the web.
Christopher M. Bishop Pattern Recognition and Machine Learning. Springer (2006)
David J.C. MacKay Information Theory, Inference, and Learning Algorithms, Cambridge University Press (2003), available freely on the web.
Course work is to be submitted via moodle in electronic form no later than 16:00 on the date due. If you are not an egineering undergraduate, please make sure you are signed up for the module on moodle, check with Laura Reed las54@cam.ac.uk, in room BNO37 if you are in doubt. Each of the three pieces of course work carry an equal weight in the evaluation. The course work will be similar, but not identical to last year's, and will be posted shortly on this web site. The duedates this year are:
Coursework #1