

Machine Learning II (2007/2008)
Dates

Fridays
18 April  27 June 2008

Time

11:0013:00

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 indepth 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.


Prerequisites

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.

Text

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,
SpringerVerlag.
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



To attend

Please contact
the unit.

