Login |
 
 

Data Mining : Artificial intelligence

Overview

Achievment: SWS : 2, Credits 4

Course type: lecture

Language: english

Exam date(Oral): 19.07.2012

Second exam date : tba

Material

Number Lecture Material
1 Introduction and Fuzzy 1 (Fri 19.04.2013) 2x2, animated
2 Fuzzy 2 (Fri 26.04.2013) 2x2, animated
3 Fuzzy 3 (Fri 03.05.2013) 2x2, animated
4 Fuzzy exercise (I) (Fri 10.05.2013) sheet
5 Neural Networks 1 (Fri 17.05.2013) 2x2, animated
6 Metaheuristics 1 (Fri 24.05.2013) 2x2, animated
7 Metaheuristics 2 (II) (Fri 31.05.2013) 2x2, animated
8 Metaheuristics exercise (Fri 07.06.2013) Sheet A, Sheet B, Interfaces
9 Stream Learning (Fri 14.06.2013) stream, streamclustering
10 Neural Networks 2 (Fri 21.06.2013) 2x2, animated
11 Daisy (Fri 28.06.2013)  
12 Genetic Fuzzy (Fri 05.07.2013) 2x2, animated
  Active Learning 2x2, animated
13 Neural Networks Exercise (III) (Fri 12.07.2013) sheet
14 EXAM (Fri 19.07.2013)  

 

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 Neural Networks
  • Learning and Analysis of Neural Networks
  • Introduction to Metaheuristics
  • Evolution of rule- and other Models
  • Hybrid 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 :

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

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

Prerequisites

Basic knowledge within the content of the Data Mining 1 lecture repectively the book Guide to Intelligent Data Analysis is advantageous.