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

Dates

Examination Dates

As usual, you have to register at the Prüfungsamt for the exams. Normally, you have to do nothing else!

We will schedule your exam during the period specified above. The personal appointments for the oral exams will be announced at our Web site on the last Tuesday before the exam week

Only if (and only then!!!) you are not available on single days of the examination period, please send an email to our secretary Fr. Ufermann. Please observe the following guidelines:

• Do not mail us earlier than 4 weeks before, and no later than 2 weeks before the exam period.
• Most likely, exams will only take place from Monday-Thursday, so requests for Friday cannot be considered.
• You should be available full-day on at least one of these days - in case you are available for a half day only, we will try our best.
• In case you registered for 2 exams, both will be held together.
• In case you are not at all available in the above period, we will try to find a separate exam date for you. Only in this case, send an email directly to Prof. Fuhr, but not before July 1.

Emails not following the rules from above will not be answered (like those saying 'Please give me an appointment for my exam in ...', or emails not originating from an uni-due.de mail account)

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 -> Information Mining
• Click the button "Beitreten"

Lecture material

• 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:
  • 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