john@cs.rhbnc.ac.UK (John Shawe-Taylor) (01/30/91)
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A SHORT COURSE
IN
NEURAL NETWORKS AND LEARNING THEORY
10th and 11th April, 1991
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Dr John Shawe-Taylor,
Department of Computer Science,
Royal Holloway and Bedford New College,
University of London,
Egham,
Surrey TW20 0EX UK
Neural networks offer the exciting prospect of training computers to
perform tasks by example rather than explicit programming. They are
finding applications across a broad spectrum of tasks including
explosives detection, credit risk, machine vision, etc. But how
reliable are such techniques? Can we guarantee that a machine that is
programmed by example will necessarily perform adequately in novel
situations? And are the techniques practical for large scale
applications? These questions are currently being addressed by research
in the area of Computational Learning Theory. This theory provides
invaluable insights for assessing the risks involved in relying on a
limited number of examples as well as providing a framework for
estimating the efficacy of training methods.
The course will cover the main results of this theory which are needed
for the practitioner. They will permit those who are developing and
using Neural Network applications to place their performance in
perspective and realistically assess how networks will scale and how
accurately they are likely to respond to new data.
A key feature of the course will be its hands-on practical flavour. It
will include sessions where participants will have an opportunity to
test out ideas in practical working examples.
The course covers two days:
Day 1: Connectionism and Neural Networks
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An overview of connectionism stressing the main strengths and
weaknesses of the approach. Particular emphasis will be given to areas
where the techniques are finding industrial application. At the same
time the areas where major problems remain to be solved will be
outlined and an indication of current trends in research will be
given.
Day 2: Learning Theory for Feedforward Networks
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The focus will be on applying recent advances in Computational Learning
Theory to Feedforward Neural Networks. An overview of the field of
Computational Learning Theory will be given. This theory puts training
problems in perspective and suggests effective solutions. It also
speaks to the question of generalisation and allows predictions of
performance to be made. The practical sessions will involve applying
these insights to the training problems of Day 1.
Who should attend?
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- Those who are involved in designing Neural Network systems or will
be required to make decisions about their application and who wish to
acquire expertise enabling them to make informed judgements about
Neural Network performance.
- Those who wish to benefit from recent advances in the theoretical
understanding of Neural Networks with a view to isolating useful areas
of current research.
Each day stands alone and delegates can enrol for either one or both days.
For more details and registration information, please write to:
Dr Penelope Smith,
Industrial Liaison Officer,
RHBNC,
Egham, Surrey TW20 0EX
or email to:
john@cs.rhbnc.ac.uk