Erasmus and Incoming Students

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Courses available in English for Exchange Students of the Degree Programme Information Technology and Systems Management

Here you can find an overview of courses that the Information Technology and Systems Management Programme offers in English for incoming exchange students in the fall/winter semester. Our exchange semesters are only offered in the fall/winter.

For further information on academic issues and provisional learning agreements, please contact the international coordinator of the Information Technology and Systems Management Programme, Thomas Schmuck (

For administrative issues please contact the Incoming Students Coordinator at the International Office (

List of classes: Fall Semester 2023/24

Applied Image and Signal Processing Master (3rd Semester)
Academic year: 2 | Course code:

Course content: The focus is on the creation of software engineering projects to cope with the digitalization of companies. Project management and software engineering skills are to be applied in the practical implementation. Among other things, business case & product innovation (using business canvas & value proposition canvas), project organization (process-oriented and agile procedure models, roles, work packages, milestones, reporting, results). The project implementation is carried out with templates from Software Engineering for the development, documentation and communication of software architectures using ARC42 (Context, Requirements, Constraints, Concept of Operations, Major building blocks/components, Block diagram, interfaces, workflow, control flow).

Students can apply theoretical and practical project management and software engineering skills in a team, based on the practical implementation of a continuous software engineering project.

Applied Image and Signal Processing Master (1st Semester)
Academic year: 1 | Course code:

Course content: Analytics, EDA Parallel Lines, Boxplots, Kernel Density Estimators, Basic Coding, Curse of Dimensionality, PCA, tSNE, Kmeans, hierarchical clustering, Spectral clustering, Distances and similarities

The module Analytics and Knowledge Discovery leads students to classical approaches on Exploratory Data Analysis for data with different kind of representation (numerical, categorical, text). For implementing a knowledge discovery process, they apply methods to reduce the dimension-ality of data, cluster it and apply various visualization methods. The course concentrates on unsupervised methodology.

Applied Image and Signal Processing Master (1st Semester)
Academic year: 1 | Course code:

Course content: Definition of Terminology in Data Science and Artificial Intelligence, Design Cycle, Extended Design Cycle, Sampling, Pre-processing, Normalization, Performance Measures, Cross Validation, Training Policies, K-nearest Neighbour and Minimum Distance Classifier, NLP Pre-processing and Features, Low Level Image Features

Upon completion of this course, students know about types and ingredients of data science projects, entitle their structure and identify different types of team members. They understand the concepts of data, models and algorithms and use specific language to describe data. They discuss the appropriateness of a data collection or intended data acquisition process with respect to a data science or artificial intelligence project. Students are introduced to the classical approach for extracting information from data with different kind of representation (numerical, categorial, one-hot or text). They collect, pre-process and visualize this data to gain basic data understand-ing. They follow the design cycle for supervised methodology by implementing data-specific feature generation, sampling of training and testing data, training selected (simple) classifiers and evaluating their performance. The students use state-of-the-art development tools and scalable technology and argue their approach content-wise.

Information Technology and Systems Management Master (3rd Semester)
Academic year: 2 | Course code:

Course content: Deep Learning Paradigm, Representation Learning, Convolutional Neural Networks, Fully Convolutional Networks, Generative Adversarial Networks, Skip Connections, Parameterisation and Model Selection/Design. Application areas: Image classification, Object detection, Image segmentation. Tools: Python, Pytorch/Tensor-Flow, Anaconda, Git, Unix/Bash, GPUs. Other aspects: Optimal use of hardware (GPUs, GPU clusters) and software resources.

Applied Image and Signal Processing Master (1st Semester)
Academic year: 1 | Course codes:

Course content: Theory of discrete signals and systems, discrete Fourier transformation, FFT, power density spectrum, discrete convolution and correlation, interpolation, calculations in z-domain, z-transfer func-tion, stability and frequency response of discrete systems, discretization of continuous systems (bilinear transformation, impulse invariant transformation), digital filters, principle and design of FIR filters, principle and design of IIR filters, IIR filter structures, quantization problems frequency transformations, simulation of signal processing algorithms and implementation of discrete sys-tems in lab environment (e.g. Matlab, Python, C)

Students understand the basic mathematical concepts to describe continuous and discrete time signals and systems and know the relations between time and frequency domain. They are famil-iar with the foundations of signal sampling and discretization and can apply important transfor-mations, e.g. Fourier-, Laplace and z-transformation. They understand basic algorithms in digital signal processing like FFT, convolution and correlation. They can transform continuous to discrete time systems e. g. with help of the impulse invariant or bilinear transformation and understand the restrictions. They have profound knowledge in designing and implementing digital filters and are also familiar with their applications. Students also have experience in simulation of DSP algorithms in a lab environment and are able to implement discrete systems with help of simulation software and low-level programming languages.

Information Technology and Systems Management Master (3rd Semester)
Academic year: 2 | Course code: AISM3ESAIL

Course content: The need for professional-ethical orientation has never been as great as it has become in the past decade. At this stage, we are being confronted with the topic of ethics from all directions: bioethics, medical ethics, animal ethics, ethics and politics, ethics and economy, ethics as a school subject instead of religion ....from personalized ethics to environmental ethics, from day-to-day to systems ethics.... our very existence seems to be sailing in a sea of ethical and morally charged issues -  particularly because the two terms -  ethics and sustainability -  are being used more and more ambiguously and prolifically. This symposium will therefore attempt to shed some light on the question of terminology and to sensitize participants to the questions behind professional ethics and sustainability.

After successfully completing the symposium, students are able to analyse and reflect on ethical-moral dilemmas; to evaluate opinions from a lecture in their own context of action; to argue social issues with a view to their own professional environment; to articulate and justify their own opinion in the group discussion.

Applied Image and Signal Processing Master (1st Semester)
Academic year: 1 | Course code:

Course content: Vector valued functions on n-dimensional domains, vector fields, scalar fields, partial derivatives, gradient operator, Jacobi and Hessian matrix, directional derivative, Taylor series in several variables, critical points, local minima, maxima and saddle points, convex optimization and applications. Integral calculus, Pre-Hilbert (inner-product) space, (orthonormal-) basis and basis transformation, Eigenvalues, Eigenvectors, matrix decompositions and applications (PCA).

Students can apply functions in several variables to model problems. They are able to analyze the change behavior of these functions and to determine critical points. They can approximate complex functions by multidimensional polynomials (especially with tangent planes and second order Taylor polynomials). They are able to use gradient based methods to find local minima. They understand selected problems of convex optimization and can solve them with mathematical software. Students are able to calculate the most important matrix decompositions and apply eigenvalue theory to perform the principal components analysis for data. Students can solve multidimensional integrals. They understand the notion of a vector space (VS) with inner product and relate to it in different application areas. They master the coordinate transformation for the change of basis in finite dimensional VSs and are familiar with the relationship to Fourier analysis. They know selected application areas of the mentioned methods.

Business Informatics Master (1st Semester)
Academic year: 1 | Course code:

Course content: The students deal with the interface between IT and management and develop prototype applications of the techniques from the field of data mining (the conversion of data into information) for decision and prognosis processes. Opportunities, technologies and framework conditions for data mining are developed as examples, by means of prognoses for the customer behaviour. The students are therefore able to develop concepts for a centralised data management and to design a clearly regulated acquisition, maintenance and utilisation management and to implement this with modern tools.

Information Technology and Systems Management Master (3rd Semester)
Academic year: 2 | Course code:

Course content: Numerical error analysis, selected numerical methods (regular and overdetermined linear systems of equations, polynomial interpolation, numerical integration and differentiation), selected geometric algorithms and data structures (convex hull, range search, Voronoi diagrams, Delaunaytriangulation, skeletal structures) and referencing to industrial applications.

International Departmental Coordinator

Porträt von: DI Schmuck Thomas , BSc
Department Information Technologies and Digitalisation
Standort: Campus Urstein
Raum: Urstein - 431
T: +43-50-2211-1333