CS-449: Neural Networks

Fall 99

Instructor: Genevieve Orr

Willamette University

Lecture Notes prepared by Genevieve Orr, Nici Schraudolph, and Fred Cummins

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Course content

Summary

Our goal is to introduce students to a powerful class of model, the Neural Network. In fact, this is a broad term which includes many diverse models and approaches. We will first motivate networks by analogy to the brain. The analogy is loose, but serves to introduce the idea of parallel and distributed computation.

We then introduce one kind of network in detail: the feedforward network trained by backpropagation of error. We discuss model architectures, training methods and data representation issues. We hope to cover everything you need to know to get backpropagation working for you. A range of applications and extensions to the basic model will be presented in the final section of the module.

Lecture 1: Introduction

Lecture 2: Classification

Lecture 3: Optimizing Linear Networks

Lecture 4: The Backprop Toolbox

Lecture 5: Unsupervised Learning

Lecture 6: Reinforcement Learning

Lecture 7: Advanced Topics

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Review for Midterm:

Links

Tutorials:

Simulators and code:

Web Sim: Java neural network simulator. Brainwave: a Java based simulator tlearn: Windows, Macintosh and Unix implentation of backprop and variants. Written in C. PDP++: C++ software with every conceivable bell and whistle. Unix only. The manual also makes a good tutorial.

Data Sources:

Related stuff of interest:

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