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Department of
Computational
Perception
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MACHINE LEARNING AND PATTERN CLASSIFICATION
344.009, KV, 3std., SS 2010
Univ.-Prof. Dr. Gerhard Widmer
This class will be taught in English.
TIME: Monday, 12:00 - 13:30
START: Monday, March 1 (*), 2010
PLACE: HS 12
(*) Make sure not to miss the first class on March 1.
This is when the groups for the practical project will be established!
Goals of this class
The lecture gives an overview of standard methods in the field of pattern
recognition, pattern classification, machine learning, and statistical data
modelling.
It covers some of the most basic concepts and methods in the field,
and demonstrates the application of these methods in a variety of
complex tasks, mainly from the area of intelligent music processing and
music information retrieval (which is the institute's special research
focus).
The lecture is accompanied by a practical track where the students
carry out a pattern classification project of real-world complexity in
several stages, from feature definition and extraction to the training
of various classifiers and systematic experimentation.
After attending the
class, the students should have a basic understanding of the central
issues in pattern classification, should be able to read the scientific
literature on learning and pattern classification, and should have
acquired the basic skills needed to
realise and evaluate pattern classification systems.
Contents
Basic notions and methods of machine learning, pattern classification,
and statistical data modelling, including
- Bayes classification and Bayes error
- Parametric densitiy estimation and maximum likelihood methods
- Non-parametric density estimation, nearest-neighbour methods
- Standard classifiers in machine learning (decision trees, rules,
Naive Bayes, support vector machines)
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- Evaluation of classifiers
- Basics of neural networks and neural computing
- Clustering and (Gaussian) Mixture Models
- Dimensionality reduction, data projection methods, Self-organising
Maps
- Markov processes and Hidden Markov Models
Course Materials
PDF versions of the powerpoint slides used in the lecture will be made
available electronically, via KUSSS.
Interested students may also want to consult the following books:
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C. Bishop (2006).
Pattern Recognition and Machine Learning.
New York: Springer Verlag.
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C. Bishop (1995).
Neural Networks for Pattern Recognition.
Oxford University Press.
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R. Duda, P. Hart, & D. Stork (2001).
Pattern Classification (2nd Edition).
NY: Wiley & Sons.
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T. Hastie, R. Tibshirani, & J. Friedman (2001).
The Elements of Statistical Learning.
New York: Springer Verlag.
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S. Russell & P. Norvig (2002).
Artificial Intelligence: A Modern Approach (2nd Edition).
Prentice-Hall.
last
edited by gw on
2010-02-04