Teaching personnel

Lecturer

• Prof. Dr.-Ing. Norbert Fuhr

Tutor(s)

• Dr. Ahmet Aker

Formalia

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

Dates

Examination Dates

Description

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.

Lecture material

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"

Lecture material

• 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 accessed/downloaded from within the university network as PDF files. .
  READ THIS BOOK!
  (The 2017 edition can be found here.)
• Clustering Tutorial
  Clustering Demo
• Video Lecture: Learning with Probabilities
• Roberto Zicari: Big Data
• Pieters: Deep Learning for NLP (Talk slides)
• Deep learning Demos:
  • Hinton's Neural Network Simulation
  • Google's Neural Network Playground
• Further reading:
  • 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.

Course structure

Lehrangebot des Fachgebiets

Introduction to IM:

• Data Mining
  • 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

Exercises