Reading directions to the books can be found here.
Book, new for 2010
Stephen Marsland: Machine Learning, an Algorithmic Perspective
Book used 2005-2009
Laurene Fausett: Fundamentals of Neural Networks
Alternative Book, free on-line
Raul Rojas: Neural Networks - A Systematic Introduction
local copy of pdf
table of contents
Lecture slides are found for the lectures via the course web
page. These slides are not 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:
This is a short intro to RBFs and their use in networks.
These are some lecture notes on RBFs and use of these in networks.
& Barto: Reinforcement Learning
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.
Harmon & Harmon: Reinforcement Learning: A Tutorial.
A pretty complete introduction to the topic.
Holst & Lansner: A Flexible and Fault Tolerant Query-Reply
System based on a Bayesian Neural Network. IJNS 1993.
This is an article about the background to the probability based
neural network and how it can be used for automatic generation of