Cambridge Neural Nets Summer School

[Richard Prager]

     The Cambridge University Programme for Industry in Collaboration
      with the Cambridge University Engineering Department Announce
            their Third Annual Neural Networks Summer School.
                          3 1/2 day short course
                           13-16 September 1993

                   BOURLARD    GEE    HINTON    JERVIS
                JORDAN    KOHONEN    NARENDRA    NIRANJAN
                  PECE    PRAGER    SUTTON    TARRASENKO

Outline and aim of the course

The course will give a broad introduction to the application and design of
neural networks and deal with both the theory and with specific
applications.  Survey material will be given, together with recent
research results in architecture and training methods, and applications
including signal processing, control, speech, robotics and human vision.
Design methodologies for a number of common neural network architectures
will be covered, together with the theory behind neural network
algorithms.  Participants will learn the strengths and weaknesses of the
neural network approach, and how to assess the potential of the technology
in respect of their own requirements.

Lectures are being given by international experts in the field, and
delegates will have the opportunity of learning first hand the technical
and practical details of recent work in neural networks from those who are
contributing to those developments.

Who Should Attend

The course is intended for engineers, software specialists and other
scientists who need to assess the current potential of neural networks.
The course will be of interest to senior technical staff who require an
overview of the subject, and to younger professionals who have recently
moved into the field, as well as to those who already have expertise in
this area and who need to keep abreast of recent developments.  Some,
although not all, of the lectures will involve graduate level mathematical
theory.

PROGRAMME

Introduction and overview: 
  Connectionist computing: an introduction and overview
  Programming a neural network
  Parallel distributed processing perspective
  Theory and parallels with conventional algorithms

Architectures:
  Pattern processing and generalisation
  Bayesian methods in neural networks
  Reinforcement learning neural networks
  Communities of expert networks
  Self organising neural networks
  Feedback networks for optimization

Applications:
  Classification of time series
  Learning forward and inverse dynamical models
  Control of nonlinear dynamical systems using neural networks
  Artificial and biological vision systems
  Silicon VLSI neural networks
  Applications to diagnostic systems
  Shape recognition in neural networks
  Applications to speech recognition
  Applications to mobile robotics
  Financial system modelling
  Applications in medical diagnostics


LECTURERS

DR HERVE BOURLARD is with Lernout & Hauspie Speech Products in
  Brussels.  He has made many contributions to the subject particularly in
  the area of speech recognition.

MR ANDREW GEE is with the Speech, Vision and Robotics Group of 
  the Cambridge University Engineering Department. He specialises in the
  use of neural networks for solving complex optimization problems.

PROFESSOR GEOFFREY HINTON is in the Computer Science Department 
  at the University of Toronto.  He was a founding member of the PDP
  research group and is responsible for many advances in the subject
  including the classic back-propagation paper.

MR TIMOTHY JERVIS is with Cambridge University Engineering 
  Department.  His interests lie in the field of neural networks and in
  the application of Bayesian statistical techniques to learning control.

PROFESSOR MICHAEL JORDAN is in the Department of Brain & Cognitive Science
  at MIT.  He was a founding member of the PDP research group and he made
  many contributions to the subject particularly in forward and inverse
  systems.

PROFESSOR TEUVO KOHONEN is with the Academy of Finland and Laboratory of
  Computer and Information Science at Helsinki University of Technology.
  His specialities are in self-organising maps and their applications.

PROFESSOR K S NARENDRA is with the Center for Systems Science in the
  Electrical Engineering Department at Yale University.  His interests are
  in the control of complex systems using neural networks.

DR MAHESAN NIRANJAN is with the Department of Engineering at Cambridge
  University.  His specialities are in speech processing and pattern
  classification.

DR ARTHUR PECE is in the Physiological laboratory at the University of
  Cambridge.  His interests are in biological vision and especially neural
  network models of cortical vision.

DR RICHARD PRAGER is with the Department of Engineering at Cambridge
  University.  His specialities are in speech and vision processing using
  artificial neural systems.

DR RICH SUTTON is with the Adaptive Systems Department of GTE Laboratories
  near Boston, USA.  His specialities are in reinforcement learning,
  planning and animal learning behaviour.

DR LIONEL TARRASENKO is with the Department of Engineering at the
  University of Oxford.  His specialities are in robotics and the hardware
  implementation of neural computing.

COURSE FEES AND ACCOMMODATION

The course fee is 750 (UK pounds), payable in advance, and includes full
course notes, a certificate of attendance, and lunch and day-time
refreshments for the duration of the course.  A number of heavily
discounted places are available for academics; please contact Renee Taylor
if you would like to be considered for one of these places.  Accommodation
can be arranged for delegates in college rooms with shared facilities at
Wolfson College at 163 (UK pounds) for 4 nights to include bed and
breakfast, dinner with wine and a Course Dinner.

For more information contact:  Renee Taylor, Course Development Manager
Cambridge Programme for Industry, 1 Trumpington Street, Cambridge CB2 1QA,
United Kingdom tel: +44 (0)223 332722 fax +44 (0)223 301122
email: rt10005 at uk.ac.cam.phx