

Module code: KIBMAT1 

4V+2U (6 hours per week) 
7 
Semester: 1 
Mandatory course: yes 
Language of instruction:
German 
Assessment:
Written exam
[updated 19.02.2018]

KIBMAT1 (P2210001) Computer Science and Communication Systems, Bachelor, ASPO 01.10.2017
, semester 1, mandatory course
PIBMA1 (P2210001) Applied Informatics, Bachelor, ASPO 01.10.2017
, semester 1, mandatory course

90 class hours (= 67.5 clock hours) over a 15week period. The total student study time is 210 hours (equivalent to 7 ECTS credits). There are therefore 142.5 hours available for class preparation and followup work and exam preparation.

Recommended prerequisites (modules):
None.

Recommended as prerequisite for:
KIBINF2 Informatics 2 KIBNRTG Introduction to Communications Engineering KIBSDSA Simulation of Discrete Systems with AnyLogic
[updated 18.02.2019]

Module coordinator:
Prof. Dr. Peter Birkner 
Lecturer: Dipl.Math. Wolfgang Braun
[updated 13.11.2016]

Learning outcomes:
Students will learn basic mathematical concepts from the areas of predictive logic, sets and figures and be able to use them confidently when formulating mathematical statements. Students will be able to reproduce basic formulas from the field of combinatorics and use these to develop solutions for combinatoric problems. They will be capable of explaining the mathematical proof concepts of direct proof, indirect proof and complete induction and thus, come up with new evidence. They will be able to enumerate the axioms of the algebraic structures group, ring and field and check the corresponding properties for structures with given operations. Students will learn the terms and statements of group theory and be able to identify them in examples of groups, such as (Z/mZ, +) and ((Z/pZ)\{0}, *). They will be able to explain vector space axioms and demonstrate them in Euclidean space. Students will be able to develop solutions in Euclidean space for geometrical problems using vector algebra, the dot product, the vector product and the triple product. They will be able to explain basic concepts of the theory of ndimensional vector spaces. They will have mastered elementary matrix calculation rules and determinant calculation rules and learn how linear images can be represented and handled using matrices. Students will be able to demonstrate how to solve a linear system and learn to master the Gauss algorithm as a method for solving linear systems. Finally, students will gain an insight into the manifold applications of mathematics in computer science (the development of programming languages, program verification, digital technology, computing accuracy on computers, cryptography, computer graphics_).
[updated 19.02.2018]

Module content:
Basic mathematical terms Propositional logic, firstorder logic, sets, especially uncountably infinite sets Relations, especially equivalence relations, partitions, functions Algebraic structures Semigroups, monoids Groups, subgroups, normal subgroups, quotient groups, homomorphisms Rings, fields, in particular Z/mZ Natural numbers, mathematical induction, recursion Peano axioms Mathematical induction Recursive definitions Binominal coefficients and binomial formulae Basic concepts of combinatorics (with quantitative considerations) Elementary vector calculation in Euclidean space Vector algebra, linear independence, dimension Vectors in coordinate systems, dot product, vector product, triple product Geometric applications Vectors in ndimensional space Generating sets, basis, subspaces Linear functions, image space, core Representation of linear functions with matrices Geometric applications: projections, reflections, rotations Matrices and linear systems Linear systems, Gaussian elimination Square matrices, matrix inversion, determinants, Cramer´s rule
[updated 26.02.2018]

Teaching methods/Media:
Lecture. An exercise sheet will be distributed every week and then discussed in small groups the following week. In addition, a tutorial will be available for work in small groups. This is voluntary. In the tutorials, students will be able work on exercises themselves (with support from the tutor, if necessary) and ask questions about the lecture material. The tutorial can also be used to fill knowledge gaps.
[updated 26.02.2018]

Recommended or required reading:
 P. Hartmann, Mathematik für Informatiker (Vieweg); can be downloaded via OPAC as a PDF.  M. Brill, Mathematik für Informatiker (Hanser).
[updated 26.02.2018]

Module offered in:
WS 2022/23,
WS 2021/22,
WS 2020/21,
WS 2019/20,
WS 2018/19,
...
