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Machine Learning

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Methods of machine learning and related areas such as Knowledge Discovery and Data Mining are central to current research in the field of intelligent systems and are already used in a variety of practical applications.

Content

Introduction and basic concepts, conceptual learning and version space, data preprocessing, case-based learning, decision trees, rule learning, Bayesian inference, Support Vector Machines, extensions and meta techniques, empirical evaluation of learning processes

Prerequisites

None. The competences taught in the following modules are recommended:

Recommended Reading

  • D.J. Hand, H. Mannila, P. Smyth. Principles of Data Mining. MIT Press. 2000.
  • T. Hastie, R. Tibshirani, J. H. Friedman. The Elements of Statistical Learning. Springer-Verlag, 2001.
  • T. Mitchell. Machine Learning. McGraw Hill, 1997.
  • I.H. Witten, E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 2000.
  • C.M. Bishop. Pattern Recognition and Machine Learning. Springer-Verlag, 2008.

Further information