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Data Mining : Artificial intelligence

Overview

Achievment: SWS : 2, Credits 4

Course type: lecture

Language: english

Exam date: Fr 22.07.2011

Second exam date : Mo 17.10.2011

Materials

Date

Type

Content

15.04.2011
Lecture
Introduction
22.04.2011 No Lecture (Karfreitag)
29.04.2011 Lecture Basics : Data Mining
06.05.2011 Lecture Fuzzy Logic (Introduction)
13.05.2011 Lecture Fuzzy Rules, Fuzzy Math
20.05.2011 Lecture Fuzzy DecTrees & Clustering, Neural Networks Intro
27.05.2011 Exercise Fuzzy exercise
03.06.2011 Lecture Artificial Neural Networks
10.06.2011 Lecture (10 sharp) RBF, PNN
17.06.2011 Exercise NN exercise
24.06.2011 Lecture Genetic algorithms and Metaheuristics
01.07.2011 Lecture Combining Systems (Neuro-Fuzzy, Fuzzy-Neuro, Genetic-Fuzzy)
08.07.2011 Exercise
15.07.2011 Lecture Recap and Q&A
22.07.2011 Oral Exam

Content

Special data mining methods in the area of soft computing and artificial intelligence.

  • Introduction to Fuzzy Logic
  • Learning from Fuzzy Logic Models
  • Introduction to Neuronale Networks
  • Learning and Analysis of Neuronal Networks
  • Introduction to Genetic Algorithms
  • Evolution of rule- and other Models
  • Hybride Methods of Soft Computing in Data Mining

 

Literature

For the lecture:

  • For all topics :

Michael Berthold, David Hand: Intelligent Data Analysis, An Introduction, 2te Auflage, Springer-Verlag, 2003.

  • Online book Fuzzy Logic (deutsch) partly very theoretical
  • Book about neuronal networks (and fuzzy systems) (german):

Detlef Nauck, Christian Borgelt, Frank Klawonn und Rudolf Kruse.: Neuro-Fuzzy-Systeme - Von den Grundlagen Neuronaler Netze zu modernen Fuzzy-Systemen Vieweg-Verlag, Wiesbaden, Germany 2003, ISBN 3-528-25265-0

     

Basics for Data Mining :

  • Tom Mitchell: Machine Learning, McGraw Hill, 1997
  • David Hand, Heikki Mannila, Padhraic Smyth: Principles of Data Mining, MIT Press 2001
  • Berthold, Borgelt, Höppner, Klawonn: Guide to Intelligent Data Analysis, Springer 2011

Course criteria

Active participation in the exercise: vote for at least 60% of the exercise, that is to agree to show the solution approach on the black board.

Oral (30 minutes) exam.

The final mark is the mark of the exam

Preliminary

Basic knowledge within the content of the Data Mining 1 lecture repectively the book Guide to Intelligent Data Analysis is advantageous, but not necessary as we repeat the most important things in the second lecture.