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Here are some other sources of information
about the topics in the course. This includes both simple and advanced
treatments of the material; the simpler texts are listed first.
- Adaptive Pattern Recognition and Neural Networks, Yoh-Han Pao
(1989).
This is a very good book. It was a textbook for the course,
but is now out of print. It is good on neural networks; less good
on Bayesian approaches. It contains more mathematical
detail and advanced material than Weiss and Kulikowski. A very good
book to get from the library and follow throughout the course.
- Neural Computing, Beale and Jackson, 1990
An elementary book on neural networks. Good treatment of Bayesian
classification, neural networks, and some of the search aspects of
the course at a simple level. Contains nothing on validation or
non-numerical methods.
- Machine learning, Neural and Statistical Classification,
D. Michie, D.J. Spiegelhalter and C. C. Taylor, 1994
Not a textbook. A collection of articles describing current
applications of and approaches to the techniques studied
in the course.
- Techniques in Computational Learning
C. J. Thornton, 1992.
A book on methods of learning from data, including supervised learning
and unsupervised learning in neural networks, and learning in decision
trees, as well as learning methods not covered in this course. It
contains nothing
on generalisation, validation, or Bayesian methods, unfortunately.
- Modern Heuristic Techniques for Combinatorial
Problems, C. Reeves (editor), 1993.
An overview of search techniques including simulated annealing and
genetic algorithms.
- Genetic Algorithms in Search, Optimization, and Machine Learning,
D. Goldberg, 1989.
The standard reference on Genetic Algorithms.
- Neural Networks, A comprehensive
Foundation, Simon Haykin, 1994. Second edition, 1998.
An excellent book on neural networks. The only problem with it is that
it contains almost no mention of
applications. Advanced and comprehensive. Second edition is very up to
date.
- Neural Networks for Pattern Recognition,
Christopher Bishop, 1996
A possible future textbook for this course -- this book contains
an excellent treatment of Bayesian approaches, neural networks, and
generalisation, as well as other aspects not treated here. Somewhat
more advanced in its treatment, its study will give a deep
understanding of the material.
- Pattern Classification and Scene Analysis, Duda and Hart, 1973
The classic work on traditional statistical methods and Bayesian methods.
Here is the alternative reading by syllabus topic.
- Bayesian Classification:
- Beale and Jackson, Chapter 2; Michie,
Spiegelhalter
and Taylor (MST)
Chapters 2,3,4; Bishop Chapters 1 and 2, Pao, Chapter 2.
- Generalisation and Validation:
- MST Chapter 7; Bishop Chapters
1.5, 9.1, 9.2, and 9.8.
- Supervised Learning Neural Networks:
- Beale and Jackson Chapters
3 and 4; MST Chapter 6; Thornton Chapters 9 - 14; Bishop Chapters 3
and 4; Pao, Chapters 5,6.
- Unsupervised Learning in Neural Networks:
- Beale and Jackson
Chapter 5; Thornton Chapter 18.
- Learning from Non-Numeric Data:
- MST Chapter 5; Thornton, Chapter 5 and
6; Pao, Chapter 4.
- Subsymbolic Search Techniques and Genetic Algorithms:
- Reeves
Chapters 1, 2, and 4;
Goldberg Chapter 2.
Next: About this document ...
Up: CS2411 - Subsymbolic Processing
Previous: Schedule of topics
Jon Shapiro
1999-09-23