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CS 542 — Machine Learning
(dt. Maschinelles Lernen)

Level, degree of commitment Advanced module, compulsory elective module
Forms of teaching and learning,
workload		 Lecture (4 SWS), recitation class (2 SWS),
270 hours (90 h attendance, 180 h private study)
Credit points,
formal requirements		 9 CP
Course requirement(s): 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 examination (individual examination) or written examination
Language,
Grading		 German,
The grading is done with 0 to 15 points according to the examination regulations for the degree program B.Sc. Data Science.
Duration,
frequency		 One semester,
Alle 3-4 Semester
Person in charge of the module's outline Prof. Dr. Dominik Heider, Prof. Dr. Matthias Schott

Contents

Machine learning and data mining methods are central to current research in intelligent systems and are already being used in a variety of practical applications.

Content: introduction and basic concepts, data preprocessing, statistical learning, case-based learning, decision trees, support vector machines, classifier ensembles, empirical evaluation of learning methods.


Qualification Goals

Students will be able to,

  • understand basic issues and goals of machine learning,
  • deal with special classes of problems, such as supervised learning (classification and regression),
  • use important machine learning methods and their scalable implementations,
  • apply concepts for the evaluation of learning methods
  • solve practical problems independently using machine learning methods,
  • to proceed according to scientific working methods (recognizing, formulating, solving problems, abstraction)
  • Speak freely about scientific content, both in front of an audience and in a discussion.

Prerequisites

None. The competences taught in the following module are recommended: Introduction to Statistics.


Applicability

Module imported from B.Sc. Data Science.

It can be attended at FB12 in study program(s)

  • B.Sc. Data Science
  • B.Sc. Computer Science
  • M.Sc. Data Science
  • M.Sc. Computer Science
  • M.Sc. Mathematics
  • M.Sc. Business Informatics
  • M.Sc. Business Mathematics
  • LAaG Computer Science

When studying M.Sc. Computer Science, this module can be attended in the study area Compulsory Elective Modules in Computer Science.

The module is assigned to Practical Computer Science. Further information on eligibility can be found in the description of the study area.


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 Winter semester 2023/24. 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.