News
First lecture on tuesday 18 october.
Dates
| Lecture | Tu 10:00 - 12:00 | C252 |
| Lecture | Fr 10:00 - 12:00 | C252 |
Lecturers
- Michael R. Berthold
- Iris Adä
- And Guests :-)
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 | Data |
| 18.11.2011 | Lecture | Introduction | Slides |
| 21.10.2011 | Lecture | Project and Data Understanding | Slides |
| 25.10.2011 | Lecture | Version Space | Slides |
| 28.10.2011 | Lecture | Introduction to KNIME | Slides, HandsOn Workflow |
| 04.11.2011 | Lecture | Modelling and Data Preprocessing | Slides |
| 11.11.2011 | Lecture | Clustering | |
| 15.11.2011 | Lecture | KNIME Node Development Intro | |
| 29.11.2011 | Lecture | Association Rules | Slides |
| 02.12.2011 | Lecture | Bayes/Regression | Slides |
| 06.12.2011 | Lecture | Decision Trees | Slides |
| 13.12.2011 | Lecture | Neural Networks | Slides |
| 10.01.2012 | Lecture | Rule Learning | Slides |
| 17.01.2012 | Lecture | Nearest Neigbor | Slides |
| 20.01.2012 | Lecture | Kernel SVM | Slides |
| 27.01.2012 | Lecture | Meta Learning | Slides |
| 06.02.2012 | Lecture | Evaluation/Deployment/LectureRun | Slides |
| All Slides | 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
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
