Teaching personnel

Lecturer

• Prof. Dr.-Ing. Norbert Fuhr
• 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
• 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

• The Data Mining part is based on the book 'Data Mining' by Ian Witten et al.. (accessible from within the university network only). .
  READ THIS BOOK!
• Other books:
  • Charu C. Aggarwal: Data Mining: The Textbook, Springer, May 2015 (PDF)
    (extensive treatment of advanced application like spatial and graph data, sequences, Web, social media, and privacy issues)
  • Jürgen Cleve, Uwe Lämmel: Data Mining. De Gruyter, 2016 (easy read, covers a subset of the Witten et al. book).
  • Shai Shalev-Shwartz, Shai Ben-David: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
  • Mohammed J. Zaki, Wagner Meira: Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, 2014.

Lecture material

• Lehrangebot des Fachgebiets
• 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.

Slides

• Chapter1-Introduction
• Chapter2-Input
• Chapter3-Output
• Chapter4-Basic Methods
• Chapter5-Evaluation
• Chapter6-Trees and Rules
• Chapter7-Instance-based and Linear Models
• Gradient Descent
• Deep Learning Lecture
• Chapter8-Data Transformations
• Chapter9-Probabilistic Methods
• Chapter11-Beyond Supervised and Unsupervised Learning
• Chapter12-Ensemble Learning
• Markov Models
• Mining Sequential Patterns
• Process Mining
• Deep Learning
• Causal Inference
• Big Data

Exercises

• Exercise sheet 1
• Exercise sheet 2
• Exercise sheet 3
• Exercise sheet 4
• Exercise sheet 5
• Exercise sheet 6
• Exercise sheet 7
• Exercise sheet 8
• Exercise sheet 9
• Exercise sheet 10
• Exercise sheet 11
• Exercise sheet 12