Weekly reading assignments.
The primary textbook will be
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).
- Pattern Recognition and Machine Learning, Chris Bishop.
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.)
methods for nonlinear classification and
regression, David Denison, Chris Holmes, Bani Mallick, and Adrian
Smith. Wiley, 2002.
Information theory, inference and learning algorithms,
Pattern Recognition, Andrew Webb, Wiley 2002 (2nd edn)
The elements of statistical learning,
Trevor Hastie, Robert Tibshirani and Jerome Friedman,
of Statistics, Larry Wasserman, Springer 2004.
- Artificial Intelligence: A
Stuart Russell and Peter Norvig, 2nd ed, Prentice Hall 2003
- Introduction to datamining, P. Tan, M. Steinbach,
V. Kumar, Addison Wesley, 2005.
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