Computational-Intelligence

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Diese Vorlesung wird nur auf englisch angeboten.

News

 The exam is corrected and can be inspected on Tuesday, 22.07.2014 in office G29-013, 10-11 a.m.

 The exam will be held on Thursday, July 17th, 2014 in Building 29 Room 307 from 9am till 11am.
 For the exam only the following aids will be allowed: pen, english-german dictionary (from the university library) 
 (or english-$native_language dictionary, if your native language is not German) and a non-graphical calculator. You do not need to bring your own paper!

Summary

General Information

This page contains information about the lecture "Intelligent Data Analysis" that is held in summer term 2014 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

 WeekdayTimeRoomBegin
LectureThursday13.15-15.45G29-K05910.04.2013
ExerciseMonday9.15-10.45G29-K05814.04.2013
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Lecturers

If you have questions regarding the lecture or exercise, please contact me (via e-mail if possible).

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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 out 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 twice 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 required to meet the certificate conditions. The exam will be oral unless more than 20 students intend to do an exam. In such a case the oral exam will be converted into a written one.

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Prerequisites

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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Slides

The lecture slides can be found below and will be constantly updated during the semester.

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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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Software

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

Information Mining tools

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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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References

  • Foundations of Intelligent Data Analysis: Making Practical Sense of Real Data
    Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
    Springer-Verlag, London, 2010
  • 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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Links

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Page last modified on July 21, 2014, at 09:42 AM by braune