Study content:

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Mathematical basics

A first important component of the degree programme is the mathematical basics. Analysis teaches the methods for examining functions (including derivatives, integrals) and for differential calculus, which are important for the representation of many real-world problems. Linear algebra teaches the concepts of vectors and matrices, which form the basis for many advanced algorithms at the interface between mathematics and numerics. In addition, the course provides insights into the areas of stochastics (mathematical modelling of probability, statistical methods for data handling), optimisation (calculation of the best possible results of mathematical models) and numerics (effective and reliable calculation methods).

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Fundamentals of Computer Science

In computer science, the foundation lectures prepare students for the systematic development of programmes and logic. Starting with an introduction to algorithmic mathematics and computer science, the computer science lectures look at methods for designing and analysing efficient algorithms (action rules).

The formal basics are learned in the subject Discrete Structures and Logic and, building on this, in the Basics of Theoretical Computer Science.The programming knowledge is looked at from the practical side in a programming practical course and deepened on realistic problems.

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Clusters and electives

The specialisation in the Mathematics and Computer Science degree programme takes place in so-called thematic clusters, each of which deals with an aspect of computer-oriented science from both a mathematical and a computer science perspective. These areas include, on the one hand, numerics and software engineering (algorithms for the reliable approximation of complex problems in natural science, technology or medicine), optimisation and algorithms (discrete or non-linear problems, Big Data), algebraic methods and formal methods (algebra, geometry and formal methods of software engineering), analysis and modelling (functional analysis, differential equations, control engineering), machine learning and self-learning systems (stochastics, statistics and artificial intelligence) and discrete-time systems (signal processing, embedded systems and communication systems).

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Electives and Bachelor Thesis

The elective area offers a number of opportunities to expand knowledge and methods in different directions beyond the content of the clusters, including formal basics, further analysis methods, but also in application areas. These options are also available in the seminar and in the research module, where the independent preparation of scientific sources or the implementation of smaller parts of research projects are practised. In the Bachelor's thesis, students can independently apply the knowledge and methods acquired in the programme to concrete problems from practice or research and present the results.

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It is recommended to start the programme in the winter semester.

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