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Machine Learning
(dt. Maschinelles Lernen)

Level, degree of commitment in original study programme Intermediate module, required module
Forms of teaching and learning,
Lecture (4 SWS), recitation class (2 SWS),
270 hours (90 h attendance, 180 h private study)
Credit points,
formal requirements
9 CP
Course requirement: Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks.
Examination type: Oral or written examination
The grading is done with 0 to 15 points according to the examination regulations for study course B.Sc. Data Science.
Original study programme B.Sc. Data Science / Informatik Aufbaumodule
One semester,
Alle 3-4 Semester
Person in charge of the module's outline Prof. Dr. Bernhard Seeger, Prof. Dr. Alfred Ultsch


Methods of machine learning and related areas such as Knowledge Discovery and Data Mining are central to current research in the field of intelligent systems and are already used in a variety of practical applications.

Content: Introduction and basic concepts, conceptual learning and version space, data preprocessing, case-based learning, decision trees, rule learning, Bayesian inference, Support Vector Machines, extensions and meta techniques, empirical evaluation of learning processes

Qualification Goals

The students shall

  • Understand the basic questions and goals of machine learning,
  • become familiar with special problem classes, such as supervised learning (classification and regression),
  • develop important methods of machine learning and their scalable implementations,
  • become familiar with concepts for the evaluation of learning methods,
  • to be enabled to solve practical problems independently using methods of machine learning,
  • practice scientific working methods (recognizing, formulating, solving problems, training the ability to abstract) and practice oral communication skills in the exercises by practicing free speech in front of an audience and during discussion.


None. The competences taught in the following module are recommended: Algorithms and Data Structures.

Recommended Reading

  • D.J. Hand, H. Mannila, P. Smyth. Principles of Data Mining. MIT Press. 2000.
  • T. Hastie, R. Tibshirani, J. H. Friedman. The Elements of Statistical Learning. Springer-Verlag, 2001.
  • T. Mitchell. Machine Learning. McGraw Hill, 1997.
  • I.H. Witten, E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 2000.
  • C.M. Bishop. Pattern Recognition and Machine Learning. Springer-Verlag, 2008.

Please note:

This page describes a module according to the latest valid module guide in Sommersemester 2021. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.