Dates
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Fridays
6 March - 29 May 2009
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Time
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13:00-15:00
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Organizers
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Yee Whye Teh (main contact) and
Maneesh Sahani.
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Location
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4th Floor Seminar Room, Alexandra House, 17 Queen Square, London
[directions]
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About the course
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This 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. The
aim of the course is to impart both a broader knowledge of the field, as
well as in-depth knowledge of certain current advanced techniques as used
in machine learning.
The course is run as a number of independent classes 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). Topics vary from year to year.
See 2007/2008 course 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.
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Prerequisites
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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.
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Text
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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.
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Topics
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Bayesian Nonparametrics (x4)
Computational Learning Theory
VC Theory and Concentration of Measures
Hilbert Space Learning Framework
Advanced Monte Carlo Sampling (x2)
Choice Models
Deep Learning
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Lecture schedule
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To attend
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Please contact Yee Whye.
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