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SummerSchool


Sun Apr 10 20:47:08 EST 2005


     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 Scie


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