Advanced Mathematics for Computer Science
- Faculty
Faculty of Engineering and Computer Science
- Version
Version 1 of 09.02.2026.
- Module identifier
11M2010
- Module level
Master
- Language of instruction
German
- ECTS credit points and grading
5.0
- Module frequency
only winter term
- Duration
1 semester
- Brief description
The use of mathematical methods forms the theoretical basis for understanding complex algorithms and methods in computer science. This module teaches students the basics of the mathematical concepts that form the basis of many applications.
- Teaching and learning outcomes
- discrete Mathematics and algebra
- probability theory
- Overall workload
The total workload for the module is 150 hours (see also "ECTS credit points and grading").
- Teaching and learning methods
Lecturer based learning Workload hours Type of teaching Media implementation Concretization 45 Lecture Presence or online - Lecturer independent learning Workload hours Type of teaching Media implementation Concretization 90 Preparation/follow-up for course work - 15 Exam preparation -
- Further explanations
Modern teaching and learning concepts, such as the inverted classroom method or agile learning scenarios, can be used as a didactic method.
- Graded examination
- oral exam or
- Written examination
- Remark on the assessment methods
The choice of examination format is determined by the instructor.
- Exam duration and scope
Graded exam performance:
Oral examination: see general part of the examination regulations
Written examination: see the associated study regulations
- Recommended prior knowledge
Mathematics modules in the bachelor's degree programs in the field of Electrical Engineering and Computer Science.
- Knowledge Broadening
The students master the basic concepts and facts of probability theory and expand their knowledge in the areas of discrete mathematics and algebra.
- Application and Transfer
Students will be able to apply algebraic elements in the context of cryptology and channel coding.
Students will be able to apply their knowledge of probability theory and discrete mathematics to algorithmic problems and will be proficient in the mathematical modeling required in the subject context.
- Literature
Die Literaturangaben beziehen sich auf die neueste Auflage, sofern nicht explizit ein Erscheinungsjahr angegeben ist.
- G. Teschl, S. Teschl, Mathematik für Informatiker, Band 1 und 2, Springer Vieweg
- D. W. Hoffmann, Einführung in die Informations- und Codierungstheorie, Springer Vieweg
- M. Artin, Algebra, Pearson
- M. Sachs, Wahrscheinlichkeitsrechnung, Hanser
- U. Krengel, Einführung in die Wahrscheinlichkeitstheorie und Statistik, vieweg studium
- Applicability in study programs
- Computer Science
- Computer Science M.Sc. (01.09.2025)
- Person responsible for the module
- Henkel, Oliver
- Teachers
- Gervens, Theodor
- Thiesing, Frank
- Ambrozkiewicz, Mikolaj
- Meyer, Jana
- Henkel, Oliver