Knowledge Discovery Group

Data Mining and Machine Learning: Basic and Advanced Techniques of Data Analysis


    Lecturer: Prof. Dr. Ansgar Scherp
    Exercises: Falk Böschen


    Lectures:   Monday        10:15 - 11:45,   CAP4 - R.1304 a
                     Wednesday   12:15 - 13:45,   CAP4 - R.1304 a
    Exercises:  Monday        12:30 - 14:00,   LMS 2 R.Ü2/K


This lecture will be taught either in German or in English (depending on the audience).


The course introduces to the topic of data mining and machine learning. It presents various different basic and advanced methods of data mining and machine learning, compares them, and shows their applications


The students will be enabled to understand, reflect, and apply different methods and techniques in the areas of data mining and machine learning. The students will be able to explain the difference and commonalities of data mining and machine learning. The students will be empowered to decide which method to apply to what kind of problem.


An introduction to the topic is conducted by giving an overview of the methods in data mining and machine learning. The commonalities and the differences between data mining and machine learning will be explained and discussed. Subsequently different selected basic and advanced methods in data mining and machine learning will be presented. In addition to the theoretical knowledge, some real-world examples of where the methods are applied will be presented.

The course covers:

  • Introduction to knowledge discovery and outline of the problem
  • Data types
  • Classification of data
  • Rule induction
  • Naïve Bayes (basic / extensions)
  • Language models
  • Hidden markov models
  • Instance-based classifier
  • Regression models
  • Artificial neural networks
  • Support vector machines
  • Ensemble classifier
  • Learning to rank
  • Clustering of data (partitional / hierarchical)
  • Learning of associaton rules from data (basic / advanced / extensions)
  • Formal concept analysis (basic / extensions)
  • Dimensionality reduction (PCA/SVD)
  • Schema induction from data


Learning Material

Slides and other learning material can be obtained from the OpenOLAT group (

Course Assessment

The exam will be oral or in written, depending on the size of the class. Active participation in the tutorials is prerequisite for admission to the exam.


The module can be accounted as WInf-DMML4 (8 ECTS) in the studies of MSc Business Informatics and MSc Computer Science.



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