Textbooks
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Weekly reading assignments.
The primary textbook will be
- Pattern Recognition and Machine Learning, Chris Bishop.
Since this book hasn't been published yet,
draft chapters will be
handed out in class to registered students.
Extra copies are in
Valerie McRae's office (CS 103).
This book is currently being revised for publication
and may contain errors.
Please send me all your comments at the end of the semester
in one large text file and I will forward them to the authors.
This will help future generations of students.
The following books may also prove useful.
(See also my review of machine learning
textbooks, written for John Kimmel, Bishop's editor.)
- Bayesian
methods for nonlinear classification and
regression, David Denison, Chris Holmes, Bani Mallick, and Adrian
Smith. Wiley, 2002.
-
Information theory, inference and learning algorithms,
David Mackay,
CUP 2003
- Statistical
Pattern Recognition, Andrew Webb, Wiley 2002 (2nd edn)
-
The elements of statistical learning,
Trevor Hastie, Robert Tibshirani and Jerome Friedman,
Springer 2001.
- All
of Statistics, Larry Wasserman, Springer 2004.
- Artificial Intelligence: A
Modern Approach,
Stuart Russell and Peter Norvig, 2nd ed, Prentice Hall 2003
- Introduction to datamining, P. Tan, M. Steinbach,
V. Kumar, Addison Wesley, 2005.
- Machine
Learning, Neural and Statistical Classification,
D. Michie, D.J. Spiegelhalter, C.C. Taylor (eds), 1994. This is
available online for free.
It compares various methods (10 years old or older) on some standard
datasets.