

Department of Engineering  
Taught By: Carl Edward Rasmussen and Zoubin Ghahramani
Code and Term: 4F13 Lent term
Year: 4th year (part IIB) Engineering, 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: 11:00  12:00 Tuesdays and 10:00  11:00 Thursdays.
Location: Lecture Room 3 (LR3), Inglis Building, Trumpington Street, Cambridge
Prerequisites: A good background in statistics, calculus, linear algebra, and computer science. 3F3 Signal and Pattern Processing and 4F10 Statistical Pattern Processing would both be useful. 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.NOTE: If you want to see lecture slides from a similar but not identical course taught last year click on the Lent 2012 course website, but be warned that the slides will change slightly this year.
This year, the exposition of the material will be centered around three specific machine learning areas: 1) supervised nonparamtric probabilistic inference using Gaussian processes, 2) the TrueSkill ranking system and 3) the latent Dirichlet Allocation model for unsupervised learning in text.
Jan 17, 27  Introduction to Machine Learning(2L): probabilistic models, inference, Bayes rule  Lecture 1 and 2 slides 
Jan 24th, 29th and 31st  Gaussian processes (3L): 
Lecture 3 and 4 slides Lecture 5 slides 
Feb 5th, 7th, 12th, 14th, 19th and 21st  TrueSkill (4L): 
Lecture 6 and 7: Introduction to Ranking Feedback on Assignment number 1 Lecture 8 and 9: Message passing on graphs 
Feb 26th, 28th, Mar 5th, 7th and 12th  LDA (5L): 
Lecture 10: Discrete distributions Lecture 11: Dirichelt distribtions and Text Lecture 12: Graphical models for Text Lecture 13 and 14: Latent Dirichlet Allocation for Topic Modelling 
COURSE WORK
Course work is to be handed in to Laura Reed in Baker BNO37 no later than 16:00 on the date due. Each of the three pieces of course work carry an equal weight in the evaluation.
Coursework #1