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First lecture on tuesday 18 october.

Overview

Leistung: SWS : 4, Credits *

Course type:  Lecture (Bachelor/Master)

Language: english

Exam: 1. 10. and 14.02.2012

2. : 26.04.2012 14:00 - 17:00

Materials

Date

Type

Content

18.11.2011 Lecture Introduction 
21.10.2011 Lecture Project and Data Understanding
25.10.2011 Lecture Version Space
28.10.2011 Lecture Introduction to KNIME
04.11.2011 Lecture Modelling and Data Preprocessing
11.11.2011 Lecture Clustering
15.11.2011 Lecture KNIME Node Development Intro
29.11.2011 Lecture Association Rules
02.12.2011 Lecture Bayes/Regression
06.12.2011 Lecture Decision Trees
13.12.2011 Lecture Neural Networks
10.01.2012 Lecture Rule  Learning
17.01.2012 Lecture Nearest Neigbor
20.01.2012 Lecture Kernel SVM
27.01.2012 Lecture Meta Learning
06.02.2012 Lecture Evaluation/Deployment/LectureRun
    All Slides

Content

The lecture series provides an introduction to Data Mining Methods with an emphasis placed on basic approaches and how they are incorporated into different problem definitions.

  • Data Mining: problem definition, motivation, application examples
  • Modelling: data-driven concept development, presentation of hypotheses
  • Version space and the evaluation of hypotheses
  • Clustering methods
  • Regression
  • Association rules

This lecture will mainly follow the book "Guide to intelligent Data Analysis". There are roundabout 25 copies of it in the library (asbest free):  UB_UniKN or get it Online@Springer

Literature

http://www.springer.com/series/3191 Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, Guide to Intelligent Data Analysis, How to Intelligently Make Sense of Real Data Series: Texts in Computer Science F. 1st Edition., 2010, XII, 397 p.

Han J., Kamber M., „Data Mining: Concepts and Techniques“, Morgan Kaufmann Publishers, August 2000.

Ester M., Sander J., „Knowledge Discovery in Databases. Techniken und Anwendungen“, Springer, 2000.

Hand D.J., Mannila H., Smyth P., „Principles of Data Mining“, MIT Press, 2001.

Mitchell T. M., „Machine Learning“, McGraw-Hill, 1997.

Witten I. H., Frank E., „Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations“, Morgan Kaufmann Publishers, 2000.

Course criteria

  • 50% of the sum of all points on the exercise sheets
  • active participation in the exercise
  • Oral exam at the end of the semester

Prerequisites

none