Neural net short course at OGI

[Hong Pi]

Oregon Graduate Institute of Science & Technology, Office of Continuing
Education, offers the short course: 

NEURAL NETWORKS: ALGORITHMS AND APPLICATIONS
June 12-16, 1995, at the OGI campus near Portland, Oregon.

Course Organizer: John E. Moody
Lead Instructor:  Hong Pi
With Lectures By: Dan Hammerstrom
         Todd K. Leen
         John E. Moody
         Thorsteinn S. Rognvaldsson 
         Eric A. Wan

      Artificial neural networks (ANN) have emerged as a new information
processing technique and an effective computational model for solving
pattern recognition and completion, feature extraction, optimization, and
function approximation problems.  This course introduces participants to
the neural network paradigms and their applications in pattern
classification; system identification; signal processing and image
analysis; control engineering; diagnosis; time series prediction; financial
analysis and trading; and speech recognition. 

      Designing a neural network application involves steps from data
preprocessing to network tuning and selection.  This course, with many
examples, application demos and hands-on lab practice, will familiarize the
participants with the techniques necessary for building successful
applications. About 50 percent of the class time is assigned to lab
sessions.  The simulations will be based on Matlab, the Matlab Neural Net
Toolbox, and other software running on 486 PCs.  Prerequisites:  Linear
algebra and calculus.  Previous experience with using Matlab is helpful,
but not required.

Who will benefit:
      Technical professionals, business analysts and other 
individuals who wish to gain a basic understanding of the theory and
algorithms of neural computation and/or are interested in applying ANN
techniques to real-world, data-driven modeling problems.
Course Objectives:
After completing the course, students will:
 - Understand the basic neural networks paradigms
 - Be familiar with the range of ANN applications
 - Have a good understanding of the techniques for designing
    successful applications
 - Gain hands-on experience with ANN modeling.

Course Outline
   Neural Networks: Biological and Artificial
      The biological inspiration.  History of neural computing.
      Types of architectures and learning algorithms.  Application
      areas.
   Simple Perceptrons and Adalines
      Decision surfaces.  Perceptron and Adaline learning rules.
      Stochastic gradient descent. Lab experiments.
   Multi-Layer Feed-Forward Networks I
      Multi-Layer Perceptrons. Back-propagation learning.
      Generalization. Early Stopping. Network performance
      analysis. Lab experiments.
   Multi-Layer Feed-Forward Networks II
      Radial basis function networks. Projection pursuit regression.
      Variants of back-propagation. Levenburg-Marquardt 
      optimization.  Lab experiments.
   Network Performance Optimization
      Network pruning techniques. Input variable selection.
      Sensitivity Analysis. Regularization. Lab experiments.
   Neural Networks for Pattern Recognition and Classification
      Nonparametric classification. Logistic regression. 
      Bayesian approach. Statistical inference.  Relation to other 
      classification methods.
   Self-Organized Networks and Unsupervised Learning
      K-means clustering. Kohonen feature mapping.  Learning   
      vector quantization. Adaptive principal components analysis.      
      Exploratory projection pursuit.  Applications. Lab experiments.
   Time Series Prediction with Neural Networks
      Linear time series models.  Nonlinear approaches.
      Case studies: economic and financial time series analysis.
      Lab experiments.
   Neural Network for Adaptive Control
      Nonlinear modeling in control.  Neural network 
      representations for dynamical systems.  Reinforcement            
      learning. Applications. Lab Experiments.
   Massively Parallel Implementation of Neural Nets on the Desktop
      Architecture and application demos of the Adaptive Solutions'
      CNAPS System.
   Summary and Perspectives

About the Instructors
Dan Hammerstrom received the B.S. degree in Electrical Engineering, with
distinction, from Montana State University, the M.S. degree in Electrical
Engineering from Stanford University, and the Ph.D. degree in Electrical
Engineering from the University of Illinois. He was on the faculty of
Cornell University from 1977 to 1980 as an assistant professor. From 1980
to 1985 he worked for Intel where he participated in the development and
implementation of the iAPX-432 and i960 and, as a consultant, the iWarp
systolic processor that was jointly developed by Intel and Carnegie Mellon
University. He is an associate professor at Oregon Graduate Institute where
he is pursuing research in massively parallel VLSI architectures, and is
the founder and Chief Technical Officer of Adaptive Solutions, Inc. He is
the architect of the Adaptive Solutions CNAPS neurocomputer.Dr.
Hammerstrom's research interests are in the area of the VLSI architectures
for pattern recognition. 

Todd K. Leen is associate professor of Computer Science and Engineering at
Oregon Graduate Institute of Science & Technology. He received his Ph.D. in
theoretical Physics from the University of  Wisconsin in 1982.  From
1982-1987 he worked at IBM Corporation, and then pursued research in
mathematical biology at Good Samaritan Hospital's Neurological Sciences
Institute.  He joined OGI in 1989.  Dr. Leen's current research interests
include neural learning, algorithms and architectures, stochastic
optimization, model constraints and pruning, and neural and non-neural
approaches to data representation and coding.  He is particularly
interested in fast, local modeling approaches, and applications to image
and speech processing. Dr. Leen served as theory program chair for the 1993
Neural Information Processing Systems (NIPS) conference, and workshops
chair for the 1994 NIPS conference.  

John E. Moody is associate professor of Computer Science and Engineering at
Oregon Graduate Institute of Science & Technology.  His current research
focuses on neural network learning theory and algorithms in it's many
manifestations.  He is particularly interested in statistical learning
theory, the dynamics of learning, and learning in dynamical contexts.  Key
application areas of his work are adaptive signal processing, adaptive
control, time series analysis, forecasting, economics and finance. Moody
has authored over 35 scientific papers, more than 25 of which concern the
theory, algorithms, and applications of neural networks.  Prior to joining
the Oregon Graduate Institute, Moody was a member of the Computer Science
and Neuroscience faculties at Yale University.  Moody received his Ph.D.
and M.A. degrees in Theoretical Physics from Princeton University, and
graduated Summa Cum Laude with a B.A. in Physics from the University of
Chicago. 

Hong Pi is a senior research associate at Oregon Graduate Institute.  He
received his Ph.D. in theoretical physics from University of Wisconsin. 
His research interests include nonlinear modeling, neural network
algorithms and applications.

Thorsteinn S. Rognvaldsson received the Ph.D. degree in theoretical physics
from Lund University, Sweden, in 1994. His research interests are Neural
Networks for prediction and classification. He is currently a postdoctoral
research associate at Oregon Graduate Institute.

Eric A. Wan, Assistant Professor of Electrical Engineering and Applied
Physics, Oregon Graduate Institute of Science & Technology, received his
Ph.D. in electrical engineering from Stanford University in 1994.  His
research interests include learning algorithms and architectures for neural
networks and adaptive signal processing.  He is particularly interested in
neural applications to time series prediction, speech enhancement, system
identification, and adaptive control.  He is a member of IEEE, INNS, Tau
Beta Pi, Sigma Xi, and Phi Beta Kappa.

For a complete course brochure contact:
Linda M. Pease, Director
Office of Continuing Education
Oregon Graduate Institute of Science & Technology
PO Box 91000
Portland, OR 97291-1000
+1-503-690-1259
+1-503-690-1686 (fax)
e-mail: continuinged at admin.ogi.edu
WWW home page: http://www.ogi.edu