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Department of
Computational
Perception
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Spezielle Kapitel aus Intelligente Systeme:
Pattern Recognition and Classification
344.009, KV, 3std., SS 2006
Univ.-Prof. Dr. Gerhard Widmer
This class will be taught in English.
TIME: Tuesday, 12:00 - 14:30
START: Tuesday, March 7, 2006
PLACE: HS 12
NEW: Grades on Task 2
Goals of this class
The lecture gives an overview of standard methods in the field of
pattern
recognition, pattern classification, 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 pattern classification and statistical
data
modelling, including
- Bayes classification and Bayes error
- Parameter estimation and maximum likelihood methods
- Density estimation and nearest-neighbour methods
- Standard classifiers in machine learning (decision trees, rules,
Naive Bayes, support vector machines)
- 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.
Interested students may also want to consult the following books:
- R. Duda, P. Hart, & D. Stork (2001). Pattern
Classification (2nd Edition).
NY: Wiley & Sons.
- C. Bishop (1995).
Neural Networks for Pattern Recognition.
Oxford University Press.
- T. Hastie, R. Tibshirani, & J. Friedman (2001).
The Elements of Statistical Learning.
New York: Springer Verlag.
- S. Russell & P. Norvig (2002).
Artificial Intelligence: A Modern Approach (2nd Edition).
Prentice-Hall.
last
edited by gw at
2006-05-16