CS 295: Pattern Recognition
This is the home page for the course CS 295: Pattern Recognition,
offered by the
Department of Computer
Science at the
University of Vermont,
Spring 2002. (N.B., the content of this page changes
frequently.)
General Information:
- Class meets on Monday, Wednesday, and Friday in Room 367 Votey,
1:25 – 2:15 p.m.
- Instructor: Robert Snapp,
353 Votey Building, (802) 656–0735.
- Here is the syllabus in pdf format.
What is pattern recognition?
Description:
Following a rigorous description of the statistical foundation of
pattern classification, this course will survey a variety of statistical
paradigms and popular pattern recognition algorithms. Topics will
include maximum likelihood estimation, Bayesian parameter estimation,
Parzen windows, hidden Markov models, linear discriminants, multilayer
neural networks, radial-basis functions, support vector machines,
decision trees, k nearest
neighbor classifiers, and k-means clustering.
Prerequisites:
This course deals frequently with
n-dimensional random variables. Thus Stat 251, or an intense
desire to learn probability theory is required. Some computer
programming exercises will be assigned.
Homework:
- Assignment 1: Read Chapters 1 and 2. Complete exercises 1, 2,
4, 6, and 13 at the end of Chapter 2. Due Friday, February 8, 2002.
- Assignment 2: Exercises 18, 23, 28, 30, 33, 43. Due February
22, 2002.
Textbook:
- Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern
Classification, Second Edition, John Wiley & Sons, New York
(2001), ISBN 0–471–05669–3. This is an updated version
of a classic textbook. The first edition was published in 1972, and
is still relevant. (I taught a course from it at Caltech in 1990.)
This new edition is even better, but is unfortunately expensive.
Other References:
- Martin Anthony and Peter L. Bartlett, Neural Network
Learning: Theoretical Foundations, Cambridge University Press,
Cambridge, U.K., 1999, ISBN 0–521–57353–X.
- Christopher M. Bishop, Neural Networks for Pattern
Recognition, Oxford University Press, Oxford, 1995,
ISBN 0198538642.
- Luc Devroye, Lazlo Gyorfi, and Gabor Lugosi,
A Probabilistic Theory of Pattern Recognition,
Springer-Verlag, New York, 1996. ISBN 0387946187.
- Keinosuke Fukunaga, Introduction to Statistical Pattern
Recognition, Second Edition,
Academic Press, 1990. 0122698517
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman,
The Elements of Statistical Learning: Data Mining, Inference,
and Prediction,
Springer-Verlag, New York, 2001, ISBN
0–387–95284–5.
- Ian Nabney,
Netlab: Algorithms for Pattern Recognition,
Springer-Verlag, New York, 2001.
- Brian D. Ripley, Pattern Recognition and Neural
Networks, Cambridge University Press, 1996. ISBN 0521460867.
Return to Robert Snapp's Home Page or to the Department of Computer
Science Home Page
Last modified: Mon Feb 11 12:31:39 EST 2002