- Targeted audience
- Angewandte Informatik Master with 6 credit points
- Komedia Master with 6 credit points
- ISE Master with 6 credit points
- BWL Master with 2+1 hours per week and 4 credit points : nur Data Mining Kap. 1-7
- Medizintechnik Master with 6 credit points
|Tuesday||12:30 - 14:55||LB/131|
|Tuesday||15:00 - 15:45||LB/131||Dr. Ahmet Aker|
|26. 02. 2018||15:00 - 17:00|
Information Mining deals with the extraction on implicit information from raw data (Data Mining) or text (Text Mining). The goal is the development of methods for analyzing databases and discovering useful information by means of abstraction. For this purpose, machine learning methods are applied.
Slides as well as sheets for the exercises can be obtained through ILIAS. For this please follow the following steps:
- Shibboleth Login -> Login with your university login information
- Scroll: Magazin -> Information Systems -> 2017 Information Mining
- Click the button "Beitreten"
- Lehrangebot des Fachgebiets
The Data Mining part is based on the book
'Data Mining' by Ian Witten and Eibe Frank.
The book chapters can be
within the university network
as PDF files.
READ THIS BOOK!
(The 2017 edition can be found here.)
- Video Lecture: Learning with Probabilities
- Roberto Zicari: Big Data
- Pieters: Deep Learning for NLP (Talk slides)
- Deep learning Demos:
- Daniel Tunkelang: 10 Things Everyone Should Know About Machine Learning
- SZ article on Data Analytics (in German): Das Erwachen, SZ vom 1.11.16
- Pedro Domingos: A few useful things to know about machine learning
- On Big Data and Data Science. Interview with James Kobielus, IBM Big Data Evangelist.
- Jürgen Cleve, Uwe Lämmel: Data Mining. De Gruyter, 2016 (easy read, covers a subset of the Witten/Frank book).
- Thomas A. Runkler; Data Mining. Vieweg+Teubner 2009
- Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2009 Series in Statistics
- Shai Shalev-Shwartz, Shai Ben-David: Understanding Machine Learning: From Theory to Algorithms
- Mohammed J. Zaki, Wagner Meira: Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, 2014.
Introduction to IM:
- Chapter 1: Introduction
- Chapter 2: Imput: Concepts, Instances, Attributes
- Chapter 3: Output: Knowledge Representation
- Chapter 4: Algoriths: The Basic Methods
- Chapter 5: Credibility: Evaluating what's been learned
- Chapter 6: Implementation: Real machine learning schemes
- Chapter 7: Data transformations
- Chapter 8: Ensemble learning
- Deep learning
- Big Data
- Mining Sequential Patterns:
- Graph Mining
- Process Mining
The sheets for the exercises can be obtained through ILIAS.