Machine Learning II (2007/2008)

Dates Fridays
18 April - 27 June 2008
Time 11:00-13:00
Lecturers Yee Whye Teh, Risi Kondor and Maneesh Sahani
Location 4th Floor Seminar Room, Alexandra House, 17 Queen Square, London [directions]
About the course

This course covers important material left out in the Gatsby Probabilistic and Unsupervised Learning course, and is targeted at students interested in machine learning research. The aim of the course is to impart both a broader knowledge of the field, as well as in-depth knowledge of current advanced Bayesian techniques as used in machine learning.

The course is run as a number of independent modules covering a range of topics. Some modules are run as taught lectures, others seminar style (presented by students), or as coding days (morning lecture followed by a day of implementing the algorithm).

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. You should thoroughly review the maths in the following cribsheet [pdf] [ps] before the start of the course. Knowledge of Matlab or Octave 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
4/18: Nonparametric Bayesian Models Yee Whye half lecture, half seminar
4/25: Nonparametric Bayesian Models Yee Whye half lecture, half seminar
5/2: Spectral Methods: Dimensionality Reduction and Clustering Maneesh lecture
5/9: Convex Optimization Yee Whye lecture
5/16: Computational Learning Theory Risi lecture
5/23: Generalized Belief Propagation Yee Whye coding day
5/30: Advanced MCMC Maneesh lecture
6/6: NIPS deadline break
6/13: VC Theory
Risi lecture
6/20: The Hilbert Space Learning Framework Risi lecture
6/27: Concentration of Measure and Prediction Bounds Risi lecture
To attend Please contact the unit.