Prof. Dr. Felix Naumann


Dr. Gjergji Kasneci


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, information theory, classification, regression, clustering, graphical models


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 - 4, 8, 9)

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

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


  • Lectures:
    • Tuesdays 13:30-15:00 in Room H-E.51
    • Every second Thursday 11:00-12:30 in Room H-E.51
  • Exercises: 
    • Every second Thursday 11:00-12:30 in Room H-E.51


TBAIntroduction & examples


Condition for exam admission: oral presentation of at least two solutions during the tutorials 

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