Newsgroups: comp.ai.neural-nets,ont.events,ut.dcs.ai
From: ronr@csri.toronto.edu (Ron Riesenbach)
Subject: Neural Networks for Industry - 2 Day Tutorial (FREE)
Organization: CSRI, University of Toronto
Date: 18 Nov 92 21:45:12 GMT


                           NEURAL NETWORKS FOR INDUSTRY

                                A 2-Day Turorial by 

                                Dr. Geoffrey Hinton

                               December 7 & 8, 1992
                             Toronto, Ontario, Canada


The  Annual Neural Networks for Industry tutorial presented by ITRC
researcher Geoffrey Hinton will now take place at the University of Toronto
instead of in Montreal.  And it's FREE.  The tutorial will still run from
9.00 am to 5.30 pm on Dec. 7 and 8, 1992.  Neural Networks for Industry '92
is intended for scientists and managers who want an understanding of the
current state-of-the-art in neural networks and the ability to make informed
decisions about whether this technology is applicable (or may soon become
applicable) to specific problems in their area of interest.  It focuses on
the basic principles underlying neural networks, and provides an overview
of the more significant learning procedures and how these can be used
effectively in current applications.

Among the topics to be covered are applications of backpropagation, ways to
improve generalization and speed convergence, competitive learning and radial
basis functions, elastic matching, unsupervised learning, and a discussion
of recent developments. To register and obtain details of hotel discounts in
Toronto, please use the attached form. Space is limited, so please register
early. This seminar is sponsored by ITRC, PRECARN and CRIM.



	  	     CONTENTS OF DAY 1

9.00-10.30  INTRODUCTION
Comparison of computers and brains
The Least Mean Squares learning procedure 
The perceptron paradigm 
Why adaptive hidden units are needed
Varieties of learning procedure 
The backpropagation algorithm 

11.00-12.30  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 

2.00-3.30  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 

4.00-5.30  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


		      CONTENTS OF DAY 2

9.00-10.30  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

11.00-12.30  COMBINING COMPETITIVE AND 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

2.00-3.30  UNSUPERVISED LEARNING AND THE MINIMUM DESCRIPTION LENGTH FRAMEWORK
The minimum description length framework
Extracting principal components  
Self-supervised  backpropagation 
Cooperative vector quantization

4.00-5.30  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 



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                                Registration Form

                          Neural Networks for Industry '92

                             by  Dr. Geoffrey Hinton

                         sponsored by ITRC, Precarn, CRIM

                               December 7-8, 1992
                             University of Toronto
                               Galbraith Building
                          35 St. George St., rm. 202
                                  Toronto, ON

(space is limited, please register early)


Name	________________________________________________________

Title		________________________________________________________

Organization________________________________________________________

Address	________________________________________________________

		________________________________________________________

City		__________________________ Postal Code___________________

Telephone	____________________________ Fax_________________________

Please fax, mail or email to:

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

Two hotels in close proximity to the University that have good rates are:
The Delta Chelsea Inn, 33 Gerrard St., W., 416/595-1975; and The Journey's End,
280 Bloor St., W., 416/968-0010.

Please mention that you are a University of Toronto visitor so that you may
obtain corporate/government rates.

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