Lecturer
Prof. Dr. Gholamreza Nakhaeizadeh

Teaching assistant
N.N.

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Time and place

Lecture
Monday 8-10 in He220

Exercise session
Monday 10-11 in He220

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Type

2 hours lecture + 1 hour exercise

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Content

• Introduction to Data Mining
• Data Mining Process:
  • Data Understanding
  • Data Pre-processing
  • Modelling
  • Model validation
• Data Mining Algorithms:
  • Regression Analysis
  • Bayesian Classifiers
  • Discriminant Analysis
  • Cluster Analysis
  • Decision and Regression Trees
  • Artificial Neural Networks
  • Association Rules

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Final exam

The final exam hasn't been scheduled yet.

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Slides

Introduction (pdf)

Process, part 1 (pdf)

Process, part 2 (pdf)

Process, part 3 (pdf)

Decision Trees (pdf)

Association Rules (pdf)

Artificial Neural Networks (pdf)

Regression Analysis, part 1 (pdf)

Naïve Bayes (pdf)

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Exercise sheets

Exercises, part 1 to 8 (pdf)

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Literature

• Hand, D.J., Mannila, H., Smyth, P.
  Principles of Data Mining
  MIT Press, 2001
• Tan, P., Steinbach, M., Kumar, V.
  Introduction to Data Mining
  Addison Wesley, 2005
• Han, J., Kamber, M.
  Data Mining, Concepts and Techniques
  Morgen Kaufmann, 2006