Cambridge Neural Networks Summer School '97

[drl@eng.cam.ac.uk]

       +-----------------------------------------------------+
       | THE SEVENTH CAMBRIDGE NEURAL NETWORKS SUMMER SCHOOL |
       +-----------------------------------------------------+

    Neural computation, network design and industrial applications 

                        September 22-24, 1997 
                   Emmanuel College, Cambridge, UK.

                    Course director: David Lovell.

This three day school provides an introduction to, and an overview of
the field of neural computation. The course is aimed at a broad range
of participants, including those needing to assess the potential of
neural networks for their own business, to those wishing to keep up to
date with recent developments.

As well as 23 presentations from international experts in the field,
the course offers a hands-on session, laboratory tour and sessions
devoted to neural network applications. Discounts are available for
academics and there are fully-funded places available for EPSRC
students.

The deadline for applications for EPSRC funding is Friday June 13, 1997. 

Full details of the course, registration and EPSRC funding
application forms are available via:
   http://svr-www.eng.cam.ac.uk/~drl/cnnss97/brochure.html


For enquiries or reservation please contact Lynda Bryers:
by 'phone on:     +44 (0)1223 302233
by fax on:        +44 (0)1223 301122
by email on:      CPI at hermes.cam.ac.uk
by post to:       University of Cambridge Programme for Industry
                  1 Trumpington Street, Cambridge CB2 1QA, UK


               List of speakers and presentation titles

Chris BISHOP 
   1.Regularization and model complexity. 
   2.Density estimation, mixture models and the EM algorithm. 
   3.(ADV) Latent variables, topographic mappings and data visualization. 

Herve BOURLARD 
   1.Statistics, neural nets and parallels with conventional algorithms. 
   2.Speech recognition. 
   3.(ADV) Applications of neural nets to speech recognition. 

George HARPUR 
   1.An introduction to unsupervised learning. 
   2.ICA and information theoretic approaches to unsupervised learning. 

David LOVELL 
   1.Neural computing in perspective (course framework). 
   2.(APP) Predicting risk in pregnancy using neural networks. 

John MOODY 
   1.Time series prediction: classical and nonlinear approaches. 
   2.Neural networks for time series analysis. 
   3.(APP) Models for economic and financial time series. 

Mahesan NIRANJAN 
   1.Neural Networks in Signal Processing. 

Richard PRAGER 
   1.Classification Trees and the CMAC. 

Rich SUTTON 
   1.Reinforcement learning I: learning to act. 
   2.Reinforcement learning II: temporal-difference learning. 
   3.(APP) Reinforcement learning III: generalization and cognition. 

Volker TRESP 
   1.Introduction to supervised learning in neural networks. 
   2.Combining neural networks: stacking, arcing, boosting, bagging,
     bragging and all that.
   3.(APP) Does it all work? Successful industrial applications of
     neural networks.

Chris WILLIAMS 
   1.Gaussian processes for regression. 
   2.(APP) Estimating wind-fields from satellite data with neural
    networks and Gaussian processes.