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 (thomas.schmuck@fh-salzburg.ac.at)

For administrative issues please contact the Incoming Students Coordinator at the International Office (international@fh-salzburg.ac.at).

List of classes: Fall Semester 2019/20

Applied Image and Signal Processing Master (1st Semester)
Academic year: 1 | Course codes:
AISM1DSPIL

Course content: 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: ITSM3ENHIL

Course content: Introduction to issues related to business ethics and sustainability, in particular with the theoretical relationship between business, economics and ethics. The importance of ethical behaviour for the daily business and its impact for the “environment” (e.g. stakeholder) is another priority. Practical case studies are connected with relationships in strategic management. In the discussion of new trends and the resulting challenges for the entrepreneur (for example in the context of "Corporate Social Responsibility") special considerations are given.

Information Technology and Systems Management Master (3rd Semester)
Academic year: 2 | Course code: ITSM3COSIL

Course content: Basics of perceptional psychology relevant for intercultural communication, definition of intercultural interactive and communications skills, interaction cases, practical application through interaction games and exercises.

Applied Image and Signal Processing Master (3rd Semester)
Academic year: 2 | Course code: AISM3PMTIL

Course content: Planning of product innovation: project definition, task structure, quality assurance, work packages, organisation, roles, phases, milestones/results, flow chart, multi-project control; implementation and controlling: conflict line/project, progress monitoring, prognosis, risk analysis, reporting system, qualitative and quantitative evaluation, documentation, software models and tools; social skills: teamwork, challenge, expectation, self-organisation, moderation, feedback, management styles, roles within a team, coaching of projects.

Applied Image and Signal Processing Master (1st Semester)
Academic year: 1 | Course code:
AISM1AKDIL

Course content: 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:
AISM1DSCIL

Course content: 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.

Applied Image and Signal Processing Master (1st Semester)
Academic year: 1 | Course code:
AISM1MAMIL

Course content: 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:
BINM1NBMIL

Course content:

International Departmental Coordinator

Porträt von: DI Schmuck Thomas, BSc
DI
  • International Departmental Coordinator, Applied Image and Signal Processing
  • International Departmental Coordinator, Information Technology & Systems Management
Standort: Campus Urstein
Raum: Urstein - 427
T: +43-50-2211-1333
E: thomas.schmuck@fh-salzburg.ac.at