Department of Computational Perception

Machine Learning and Pattern Recognition

344.009, KV, 3std., SS 2013

Univ.-Prof. Dr. Gerhard Widmer

This class will be taught in English.

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

• 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, ...)
• Basics of neural networks and neural computing
• Classifier evaluation and model selection
• Clustering and (Gaussian) Mixture Models
• Dimensionality reduction, data projection methods, Self-organising Maps
• Markov processes and Hidden Markov Models

Course Material

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:

• C. Bishop (2006). Pattern Recognition and Machine Learning. New York: Springer Verlag.
• C. Bishop (1995). Neural Networks for Pattern Recognition. Oxford University Press.
• R. Duda, P. Hart, & D. Stork (2001). Pattern Classification (2nd Edition). NY: Wiley & Sons.
• 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.