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Data Mining and Probabilistic Reasoning (Wintersemester 2013/2014)



Data arising from business transactions, scientific measurements and other forms of content-creation calls for automatic data mining and pattern recognition techniques that allow us to efficiently make sense of this data. At the same time these techniques should be able to handle uncertainty, as data from measurements may be imprecise and user-generated content may be unreliable.

This lecture will introduce the main concepts of data mining and probabilistic reasoning, ranging from basic probability and information theory to popular classification and clustering algorithms. An introduction to the exciting area of graphical models and probabilistic inference will highlight the link between uncertainty and probabilistic learning models.



Probability theory & statistical methods
Information theory
Evaluation measures
Hierarchical classifiers
Linear classifiers
Artificial neural networks
Hierarchical clustering
Co-clustering, topic models
Graphical models: introduction
Directed vs. undirected models
Factor graphs & inference
Example: Hidden Markov Models
Reinforcement learning


I. H. Witten, E. Frank, M. A. Hall: Data Mining Practical Machine Learning Tools and Techniques (Chapters 1 - 6)

C. Bishop: Pattern Recognition and Machine Learning (Chapters 1, 2, 4, 8, 9)

D. J. C. MacKay: Information Theory, Inference and Learning Algorithms (Chapters 1 - 6)

P. Flach: Machine Learning – The Art and Science of Algorithms that make Sense of Data (Chapters 1 – 3, 5 – 11)

T. M. Mitchell: Machine Learning (Chapters 3 - 6, 8, 10)

Lern- und Lehrformen

There will be biweekly exercise sessions, starting from 24.10.2013.


Form of exam: oral exam at the end of the term

Condition for exam admission: oral presentation of at least one solution during the tutorials


Tuesdays: 13:30 - 15:00 (Room H-E.51)

Thursdays: 11:00 - 12:30 (Room H-2.57)

Allgemeine Information

  • Semesterwochenstunden : 4
  • ECTS : 6
  • Benotet : Ja
  • Einschreibefrist : 1.10.2013 - 31.10.2013
  • Programm : IT-Systems Engineering MA
  • Lehrform : VU
  • Belegungsart : Wahlpflicht


  • Operating Systems & Information Systems Technology
  • Software Architecture & Modeling Technology