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
Introduction and overview:
Connectionist computing: an introduction and overview
Programming a neural network
Parallel distributed processing perspective
Theory and parallels with conventional algorithms
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
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
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
PROFESSOR TEUVO KOHONEN is with the Academy of Finland and Laboratory of
Computer and Information Scie