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Lecture Intelligent Data Analysis

Summer Term 2009


Regular lecture on July 8th.

Exam dates below.

Link to the Information Miner.


General Information

This page contains information about the lecture "Intelligent Data Analysis" that is held in summer term 2009 by Prof. Dr. Rudolf Kruse. This page is updated during the course.

In many areas the overwhelmingly large volumes of data can hardly be assessed manually by a user: Think of market basket data collected in supermarkets. The analysis of this data however is important because they may reveal decisive information about e.g. the customers' buying behavior. Therefore, "intelligent" analysis techniques have to be devised that are capable to extract from illegible date comprehensible information.

In this lecture a multitude of methods are introduced that range from classical statistics (descriptive statistics, inferential statistics, parameter estimation, regression) over association rules, Bayesian classifiers, decision and regression trees, fuzzy data analysis to clustering techniques.

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Schedule and Rooms

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If you have questions regarding the lecture or exercise, please contact (via e-mail if possible) one of the persons named below.

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Conditions for Certificates (Scheine) and Exams

Certificate (Übungsschein): There are assignment sheets published every week. Assignments the solutions of which you want to present in the exercise lecture have to be ticked beforehand on a votation sheet that is handed our prior to every exercise lecture. If ticked, you may be asked to present your solution in front of class. The solutions need not necessarily be completely correct, however, it should become obvious that you treated the assignment thoroughly. You are granted the certificate (Schein), if (and only if) you

  • ticked at least two thirds of the assignments,
  • presented at least two times a solution during the exercise, and
  • passed a small colloquium (approx. 10 min) or a written test (if there are more than 20 students) after the course.

Exam: If you intend to finish the course with an exam, your are not required to meet the certificate conditions. However, you are of course encouraged to also solve the assignments. Regarding the exam, please register with the lists lying out at the Prüfungsamt. Dates see below. The exam has to be announced to the Prüfungsamt two weeks prior to the exam date via this application form. The exam consists of a 20 to 25 minutes oral examination about the subjects presented during the course. Emphasis is put on understanding rather than formal details. The final marks will be in the following range: 1.0, 1.3, 1.7, ..., 3.7, 4.0, 5.0. If more than 20 students intend to do an exam, the oral exam may be converted into a written examination.

Exam dates (for time slots see list at the Prüfungsamt): 15.07., 16.07., 09.09., 10.09.

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You should have background knowledge on fundamentals of computer science such as algorithms, data structures etc. Also, some insights into probability theory are highly recommended.

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The lecture slides will be published here incrementally as the course proceeds.

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Assignment Sheets

The assignment sheets will be published weekly at this location.

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Additional Material

Feel free to check out the following supplementary material that augment the lecture and exercise.

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Here you find links to programs with wich simple data analysis tasks can be solved.

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Data Sets

Here you find some example data sets that can be fed into the algorithms above.

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  • Intelligent Data Analysis: An Introduction (2. edition)
    D. Hand and M. Berthold (eds.)
    Springer-Verlag, Berlin, 2002
  • Elementare Einführung in die angewandte Statistik
    K. Bosch
    Vieweg, Wiesbaden, 2000
  • Angewandte Statistik (9. Auflage)
    L. Sachs
    Springer, Berlin, 1999
  • Machine Learning
    T. Mitchell
    McGraw Hill, New York, NY, USA 1997
  • Data Mining Techniques. For Marketing, Sales, and Customer Support.
    M.J. Berry and G.S. Linoff
    J. Wiley & Sons, Chichester, United Kingdom 1997
  • Data Mining - Theoretische Aspekte und Anwendungen
    G. Nakhaeizadeh (Hrsg.)
    Physica-Verlag, Heidelberg, 1998
  • Data Mining Methods for Knowledge Discovery
    K. Cios, W. Pedrycz, R. Swiniarski
    Kluwer, Dordrecht, Netherlands 1998
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Page last modified on July 01, 2009, at 01:03 PM by msteinbr