Newsgroups: comp.ai.neural-nets,ont.events,can.ai
From: ronr@csri.toronto.edu (Ron Riesenbach)
Subject: Neural Nets for Industry - 2 Day course
Organization: CSRI, University of Toronto
Date: 9 Oct 92 18:02:19 GMT


                                 Neural Networks
                                      for
                                   Industry

                                TWO-DAY TUTORIAL

                                  presented by:
                               Dr. Geoffrey Hinton

                                  Sponsored by:
                   Information Technology Research Centre (ITRC)
                               PRECARN Associates Inc.
                                      and
               Centre de Recherche Informatique de Montreal (CRIM)

December 7 and 8, 1992
Queen Elizabeth  Hotel
900 Rene Levesque Blvd. West
Montreal, Quebec


Why Neural Networks?

Serial computation has been very successful at tasks that can be
characterized by clean logical rules.  It has been much less successful at
tasks like real-world perception or common sense reasoning.  These tasks
typically require massive amounts of uncertain evidence which need to be
combined in complex ways to reach reliable decisions.  The brain is
extremely good at these computations and there is now a growing consensus
that massively parallel "neural" computation may be the best way to solve
these problems.

Interest in artificial neural networks has been rekindled by the discovery
of powerful new learning procedures which allow multilayer, non-linear
networks to learn from examples.  The learning procedures automatically
construct the internal representations that the networks require and so
they remove the need for explicit programming.

Who Should Attend?

The tutorial is intended for Industry Researchers and Managers who would
like to understand the basic principles underlying neural networks.  The
tutorial will explain the main learning procedures and show how these can
be used effectively in current applications.  No previous exposure to
neural networks is necessary although a degree in computer science or
electrical engineering (or equivalent experience) is desirable.  Those
attending can expect to gain an understanding of the current
state-of-the-art in neural networks and be in a position to make informed
decisions about whether this technology is currently  applicable, (or may
soon become applicable) to specific problems in their area of interest.

Format

This tutorial consists of a two-day series of lectures which systematically
cover the underlying principles and practical applications of neural
networks to real-world problems.  Course notes will be provided.


Tutorial Content

Day 1
INTRODUCTION
Comparison of Computers and brains
The Least Mean Square learning procedure
The perceptron paradigm
Why adaptive hidden units are needed
Varieties of learning procedure
The backpropagation algorithm

SOME APPLICATIONS OF BACKPROPAGATION
The NetTalk example
Detecting bombs in suitcases
Following a road with an autonomous land vehicle
Recognizing cancerous cells in Pap-smears
Removing atmospheric distortion from telescopes
Predicting biochemical processes
Cross-entropy error functions
Adaptive interfaces:  The Glove-Talk example
Ways to incorporate expert knowledge
Forward and inverse models for control

IMPROVING GENERALIZATION AND SPEEDING LEARNING
Theoretical results on generalization
Simplicity and generalization
Varieties of weight decay
Cross-validation
Ways of making backpropagation learn faster
Line search and conjugate gradient
Cascade-correlation and an application
Guidelines for applying backpropagation

MORE APPLICATIONS OF BACKPROPAGATION
Recognizing phonemes in spectrograms
Recognizing hand-printed digits
Hidden Markov Models
Learning the probabilities in HMM's
Combining neural nets and HMM's

Day 2
COMPETITIVE LEARNING AND MAP FORMATION
Finding clusters with competitive learning
Soft competitive learning
The EM algorithm
Kohonen's method of forming topographic maps
Methods of forming maps for autonomous robots

COMBINING COMPETITIVE LEARNING WITH SUPERVISED LEARNING
Radial basis function networks
Methods of choosing the basis functions
Some comparisons of learning speed
Communities of expert networks
A vowel recognition example

UNSUPERVISED LEARNING AND THE MINIMUM DESCRIPTION LENGTH FRAMEWORK
The minimum description length framework
Extracting principal components
Self-supervised backpropagation
Cooperative vector quantizaton

MISCELLANEOUS RECENT DEVELOPMENTS AND GENERAL INFORMATION
A better way of keeping the weights simple
The Bayesian approach:  Ensembles of networks
Backpropagation through time and HMM's
Simulators, workstations, boards, and chips
Neural network journals and conferences

Biography

Dr. Geoffrey E. Hinton (instructor)

Geoffrey Hinton is Professor of Computer Science at the University of
Toronto, the Noranda Fellow of the Canadian Institute for Advanced
Research, a principal researcher with the Information Technology Research
Centre and a project leader with the Institute for Robotics and Intelligent
Systems (IRIS).  He received his PhD in Artificial Intelligence from the
University of Edinburgh.  He has been working on computational models of
neural networks for the last seventeen years and has published 80 papers
and book chapters on applications of neural networks in vision, learning,
and knowledge representation.  These publications include the book
"Parallel Models of Associative Memory" (with James Anderson) and the
original papers on distributed representations, on Boltzmann machines (with
Terrence Sejnowski), and on back-propagation (with David Rumelhart and
Ronald Williams).  He is also one of the major contributors to the
collection "Parallel Distributed Processing" edited by Rumelhart and
McClelland.

Dr. Hinton is on the governing boards of the Cognitive Science Society and
the American Association for Artificial Intelligence.  He is a member of
the editorial boards of the journals Artificial Intelligence, Machine
Learning, Cognitive Science, Neural Computation and Computer Speech and
Language.

Dr. Hinton is an expert at explaining neural network research to a wide
variety of audiences.  He has given invited lectures on the research at
numerous international conferences, workshops, and summer schools.  He has
given industrial tutorials in the United States for the Technology Transfer
Institute, AT&T Bell labs, Apple, Digital Equipment Corp., and the American
Association for Artificial Intelligence.

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- Registration Form -

Neural Networks for Industry
Tutorial by Geoffrey Hinton
December 7-8, 1992
The Queen Elizabeth Hotel
900 Rene Levesque Blvd., W.
Montreal, Quebec

Name          _________________________________________

Title         _________________________________________

Organization  _________________________________________

Address       _________________________________________

              _________________________________________

              _________________________________________

Postal Code   __________________

Telephone     __________________ Fax ___________________

Email         _________________________________________

Registration Fee (choose one):

ITRC, PRECARN or CRIM members          o  $350.00
Non-members                            o  $500.00

Make cheques payable to "Information Technology Research Centre"

REGISTRATION DEADLINE:  October 31, 1992.
(Space is limited so register early)

Please fax, mail or email your registration form to either of:

ITRC, Rosanna Reid      
University of Toronto        
D.L. Pratt Bldg., Rm. 286        
6 King's College Rd.          
Toronto, Ontario M5S 1A1    
phone  (416) 978-8558      
fax   (416) 978-7207          
email:  rosanna@itrchq.itrc.on.ca

or

PRECARN, Lise McCourt
300-30 Colonnade Rd.
Nepean, ON
K2E 7J6
phone (613) 727-9576
fax (613) 727-5672
email: gavrel@a1.atott2.nrc.ca
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Newsgroups: comp.ai.neural-nets