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Module guide WS 2018-2022

Module MA4660

Prognostic models (PM)

Duration:


1 Semester
Turnus of offer:


irregularly
Credit points:


4
Course of studies, specific field and terms:
  • Master MES 2011 (optional subject), mathematics, 1st or 2nd semester
  • Master CLS 2010 (optional subject), mathematics, Arbitrary semester
Classes and lectures:
  • Prognostic models (exercise, 1 SWS)
  • Prognostic models (lecture, 2 SWS)
Workload:
  • 45 Hours in-classroom work
  • 15 Hours exam preparation
  • 60 Hours private studies
Contents of teaching:
  • Aims and applications of prognostic models
  • General approach to develop valid prognostic models
  • Classical statistical approaches to develop prognostic models
  • Approaches to validate prognostic models
  • Alternative approaches to develop prognostic models: Classification and Regression Trees, ensemble methods, support vector machines
Qualification-goals/Competencies:
  • Understanding the application as well as the general approach to develop valid prognostic models
  • Mastering the most important classical statistical approaches to develop prognostic models
  • Mastering the most important alternative approaches to develop prognostic models
  • Mastering different methods to validated prognostic models
  • Applying basic approaches by hand and more complex approaches computer-based
Grading through:
  • written exam
Requires:
Responsible for this module:
  • Prof. Dr. rer. nat. Andreas Ziegler
Teachers:
  • Prof. Dr. rer. biol. hum. Inke König
  • Prof. Dr. rer. nat. Andreas Ziegler
Language:
  • offered only in German
Letzte Änderung:
17.7.2019