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Module guide

Module MA2510-KP04, MA2510

Stochastics 1 (Stoch1)

Duration:


1 Semester
Turnus of offer:


each summer semester
Credit points:


4
Course of studies, specific field and terms:
  • Bachelor MES 2020 (optional subject), mathematics / natural sciences
  • Bachelor Computer Science 2019 (compulsory), mathematics, 4th semester
  • Bachelor Robotics and Autonomous Systems 2020 (compulsory), mathematics, 4th semester
  • Bachelor Medical Informatics 2019 (optional subject), mathematics, 4th to 6th semester
  • Minor in Teaching Mathematics, Bachelor of Arts 2017 (compulsory), mathematics, 8th semester
  • Bachelor Computer Science 2016 (compulsory), mathematics, 4th semester
  • Bachelor CLS 2016 (compulsory), mathematics, 2nd semester
  • Bachelor Robotics and Autonomous Systems 2016 (compulsory), mathematics, 4th semester
  • Bachelor IT-Security 2016 (compulsory), mathematics, 2nd semester
  • Bachelor Biophysics 2016 (optional subject), mathematics, 6th semester
  • Bachelor Medical Informatics 2014 (optional subject), mathematics, 5th or 6th semester
  • Bachelor MES 2014 (optional subject), mathematics / natural sciences, 4th or 6th semester
  • Bachelor Computer Science 2014 (compulsory), mathematics, 4th semester
  • Bachelor Computer Science 2012 (compulsory), mathematics, 4th semester
  • Bachelor MES 2011 (compulsory), mathematics, 4th semester
  • Bachelor CLS 2010 (compulsory), mathematics, 2nd semester
  • Bachelor CLS 2023 (compulsory), mathematics, 2nd semester
  • Minor in Teaching Mathematics, Bachelor of Arts 2023 (compulsory), mathematics, 8th semester
Classes and lectures:
  • Stochastic 1 (exercise, 1 SWS)
  • Stochastics 1 (lecture, 2 SWS)
Workload:
  • 10 Hours exam preparation
  • 65 Hours private studies and exercises
  • 45 Hours in-classroom work
Contents of teaching:
  • probability spaces
  • basics of combinatorics
  • conditional probability and stochastic independency
  • random variables
  • important discrete and continuous one-dimensional probability distributions
  • characteristics of distributions
  • law of large numbers, central limit theorem
  • modeling examples from the life sciences
Qualification-goals/Competencies:
  • Students are able to explain basic stochastic models formally correct and in the context of their application
  • They are able to formalize stochastic problems
  • They are able to identify basic combinatorial patterns and to use them for solving stochastic problems
  • They understand central statements of elementary stochastics
Grading through:
  • written exam
Is requisite for:
Responsible for this module:
  • Prof. Dr. rer. nat. Karsten Keller
Teachers:
  • Prof. Dr. rer. nat. Karsten Keller
Literature:
  • N. Henze: Stochastik für Einsteiger - Vieweg
  • U. Krengel: Einführung in die Wahrscheinlichkeitstheorie - Vieweg
Language:
  • offered only in German
Notes:

Prerequisites for attending the module:
- None

Prerequisites for the exam:
- Successful completion of homework assignments during the semester.

Letzte Änderung:
19.11.2021