Machine Learning II & Machine Learning Journal Club (2009/2010)

Dates Mondays (MLII) and Tuesdays (MLJC)
18 January - 10 May 2010
Time 12:00-14:00
Organizers Yee Whye Teh.
Location 4th Floor Seminar Room, Alexandra House, 17 Queen Square, London [directions]
About the course

MLII is a topics-oriented course covering important material left out in the Gatsby Probabilistic and Unsupervised Learning course, and is targeted at students interested in machine learning research. MLJC is a journal club covering advanced material in machine learning. This semester we are combining the two, with MLII on Mondays covering the introductory material and MLJC the advanced material. The aim is to impart both a broader knowledge of the field, as well as in-depth knowledge of certain current advanced techniques.

MLII & MLJC will be run as a number of independent classes covering a range of topics. Please make sure you read up on the linked reading materials before class as this year we are running most lectures journal club style.

See 2007/2008 and 2008/2009 course schedules here.

The course is run primarily for new Gatsby machine learning students for whom it is mandatory. Students, postdocs and faculty from both within and outside the unit are welcome to attend, but should contact Yee Whye in advance.


A good background in statistics, calculus, linear algebra, and computer science, as well as familiarity with the material in the Probabilistic and Unsupervised Learning course. The cribsheet [pdf] [ps] gives some useful mathematical knowledge required for the course. Knowledge of Matlab, Octave or some other development language will be required for the coding days.


There is no required textbook. However, the following are excellent introductory texts for many of the topics covered here.

David J.C. MacKay (2003) Information Theory, Inference, and Learning Algorithms, Cambridge University Press. (also available online).
Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer-Verlag.
Stephen Boyd and Lieven Vandenberghe (2004) Convex Optimization, Cambridge University Press. (also available online).
Bernhard Scholkopf and Alex Smola (2002) Learning with Kernels, MIT Press.
Specific reading are listed in the schedule below. Information on specific modules will be updated later.
Lecture schedule
Date Monday Tuesday
18/01: Bayesian Nonparametrics Intro I (Teh) Bayesian Nonparametrics Intro II (Teh)
25/01: MCMC for DP Mixture (Gorur, Rao) Ferguson, Antoniak, Blackwell & McQueen (Gorur, Rao, Teh)
01/02: Blackwell-MacQueen (Gorur), Completely Random Measures (Gorur, Blundell) Counting Processes (Gorur, Gasthaus, Teh)
08/02: Some of Statistics (Silva) Loopy BP & graph zeta (Blundell, Gasthaus)
15/02: Some of Statistics (Silva) Advanced Graphical Models (Sun, Furmston, Silva)
22/02: Causality (Silva, Blundell) Advanced Graphical Models (Gasthaus)
01/03: POMDPs (Blundell, Teodoru) POMDPs (Blundell, Teodoru)
08/03: No classes: PhD Interviews
15/03: Kernels (Gretton?) Kernels (Gretton?)
22/03: No classes: Quinquennial
29/03: Deep Belief Nets (Teh) Deep Belief Nets (Teh)
05/04: No classes: Easter
12/04: Consistency of Approximate Inference (Teh) Consistency of Approximate Inference (Teh)
19/04: SDEs (Beskos?) Combinatorial Optimization (Kolmogorov?)
26/04: Dimensionality Reduction (Sahani) Dimensionality Reduction (Sahani)
03/05: TBA TBA
10/05: TBA TBA
To attend Please contact Yee Whye.