Reading material
Reading directions
Reading directions to the books can be found here.
Book, new for 2010
Stephen Marsland: Machine Learning, an Algorithmic Perspective
2009, CSC-Press
ISBN 1420067184
Book used 2005-2009
Laurene Fausett: Fundamentals of Neural Networks
1994, Prentice-Hall
ISBN 0-13-334186-0
Alternative Book, free on-line
Raul Rojas: Neural Networks - A Systematic Introduction
website
local copy of pdf
table of contents
Additional material
Lecture notes are found for the lectures via the course web
page. These are not complete notes, so you must add you own notes
to these for them to be useful.
For those who want to read more, the following sources may be of interest:
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This is a short intro to RBFs and their use in networks.
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These are some lecture notes on RBFs and use of these in networks.
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Sutton
& Barto: Reinforcement Learning
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Richard Suttons and Andrew Bartows classic
book about reiforcement learning. Ths is one of the best books
in this field. Note that the complete book also is available on-line.
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Harmon & Harmon: Reinforcement Learning: A Tutorial.
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A pretty complete introduction to the topic.
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Holst & Lansner: A Flexible and Fault Tolerant Query-Reply
System based on a Bayesian Neural Network. IJNS 1993.
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This is an article about the background to the probability based
neural network and how it can be used for automatic generation of
queries.