[comp.ai.neural-nets] Short Course: Artificial Neural Nets

chucko@saturn.ucsc.edu (Chuck Stein) (06/11/88)

                The University of California
                     Eighteenth Annual
               INSTITUTE IN COMPUTER SCIENCE
                    presents courses in:

   * Scientific Visualization    * Fault Tolerant Computing
   * Parallel Computation        * Image Engineering
   * Data Compression            * Machine Learning

                             at
                   Techmart, Santa Clara
                            and
                  on campus in Santa Cruz

Following is a course description for:
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                    Artificial Neural Networks
                          August 1-3

Instructor:  BART KOSKO
X415 Computer & Information Sciences (2)

This course offers a rigorous introduction to the mechanics of 
artificial neural networks.  It is aimed at an interdisciplinary audience 
with emphasis on engineering and artificial intelligence.  Designed as 
an active process, the course will oblige participants to undertake 
assignments including written work.  Upon completion, attendees will 
have a working knowledge of several state-of-the-art neural network 
technologies.

Overview :
Artificial neural networks are programmable dynamical systems.  Their 
global properties can often be designed to carry out practical 
information processing--pattern storage, robust recall, fuzzy 
association, distributed prediction, inductive inference, and 
combinatorial optimization.  Artificial neural networks are especially 
well suited for realtime pattern recognition and nearest neighbor 
matching in large databases.  Some continuous and diffusion networks 
can perform global optimization.  Some networks can learn complex 
functional mappings simply by presenting them with input-output 
pairs.  Some fuzzy knowledge networks can represent, propagate, and 
infer uncertain knowledge in contexts where traditional AI decision-
tree graph search cannot be applied.

Prerequisite:  Background in calculus, matrix algebra, and some 
probability theory.

 Schedule                             
 Monday:                                
*Associative Memory
  symbolic vs. subsymbolic processing
  preattentive and attentive processing
  global stability
  bidirectional associative memories (BAM)
  optical BAMs
  error-correcting decoding
  temporal associative memory, avalanches
  optimal linear associative memory

Tuesday:
*Global Stability and Unsupervised Learning
  continuous BAMs and the Cohen-Grossberg Theorem
  neurocircuits for combinatorial optimization
  Hebb, differential Hebb, and competitive learning
  adaptive BAMs
  Grossberg Theory
  adaptive resonance theory
  adaptive vector quantization
  counter-propagation

Wednesday:
*Supervised Learning and Fuzzy Knowledge Processing
  lean-mean-square algorithm
  backpropagation
  simulated annealing
  Geman-Hwang theorem for Brownian diffusions
  Cauchy vs. Boltzmann machines
  fuzzy entropy and conditioning                        
  fuzzy associative memories (FAMs)
  fuzzy cognitive maps (FCMs) and learning FCMs

Instructor:  BART KOSKO, Assistant Professor of Electrical 
Engineering at the University of Southern California

Fee:  Credit, $895 (EDP J2478)

Dates:  Three days, Mon.-Wed., Aug. 1-3, 9 a.m.-5 p.m.

Place:  Techmart, 5201 Great America Pkwy., Santa Clara

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RESERVATIONS:
Enrollment in these courses is limited.  If you wish to attend a course 
and have not pre-registered, please call (408) 429-4535 to insure that 
space is still available and to reserve a place.

DISCOUNTS:
Corporate, faculty, IEEE member, and graduate student discounts and
fellowships are available.  Please call Karin Poklen at (408) 429-4535
for more information.

COORDINATOR:
Ronald L. Smith, Institute in Computer Science, (408) 429-2386.

FOR FURTHER INFORMATION:
Please write Institute in Computer Science, University of California 
Extension, Santa Cruz, CA 95064, or phone Karin Poklen at (408) 429-
4535.  You may also enroll by phone by calling (408) 429-4535.  A
packet of information on transportation and accommodations will be sent
to you upon receipt of your enrollment.