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Full version is available at University of Salzburg
Link: full version of the curriculum
Following please find the courses taught at Salzburg University of Applied Sciences:
Applied Image and Signal Processing
Digital Signal Processing 1
| Semester | 1 |
|---|---|
| Studienjahr | 1 |
| Lehrveranstaltungsnummer | AISM1DSPPT |
| Typ | PT |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 1 |
| ECTS-Punkte | 2 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Solution of project based exercises related to the content of the lecture Digital Signal Processing 1 ILV.
Übergeordnetes Modul:
Digital Signal Processing 1
Kompetenzerwerb aus dem übergeordneten Modul:
Students are able to 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 familiar with the foundations of signal sampling and discretisation and can apply important transformations, e.g. Fourier-, Laplace and z-transformation. They can transform continuous to discrete time systems e. g. with help of the impuls invariant or bilinear transformation and understand the restrictions. They understand basic algorithms in digital signal processing like FFT, convolution and correlation. They have profound knowledge in designing an implementing digital filters and are also familiar with their applications. They understand the operation of A/D and D/A converters and are able to simulate continuous and discrete time systems with standard tools. They have experience in measuring and analysing signal and system properties in a Lab environment.
Digital Signal Processing 1
| Semester | 1 |
|---|---|
| Studienjahr | 1 |
| Lehrveranstaltungsnummer | AISM1DSPIL |
| Typ | IL |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 3 |
| ECTS-Punkte | 4 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Theory of discrete signals and systems: discrete Fourier transformation (FFT), power density spectrum, discrete convolution and correlation, interpolation, implementation in Matlab and C z-transform, z transfer function, stability and frequency response of discrete systems, discretisation of continuous systems (bilinear transformation, impulse invariant transformation) Digital filters: principle and design of FIR filters, simulation with simulation tools (e.g. Matlab), principle and design of IIR filters, filter implementation in Matlab and C.
Übergeordnetes Modul:
Digital Signal Processing 1
Kompetenzerwerb aus dem übergeordneten Modul:
Students are able to 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 familiar with the foundations of signal sampling and discretisation and can apply important transformations, e.g. Fourier-, Laplace and z-transformation. They can transform continuous to discrete time systems e. g. with help of the impuls invariant or bilinear transformation and understand the restrictions. They understand basic algorithms in digital signal processing like FFT, convolution and correlation. They have profound knowledge in designing an implementing digital filters and are also familiar with their applications. They understand the operation of A/D and D/A converters and are able to simulate continuous and discrete time systems with standard tools. They have experience in measuring and analysing signal and system properties in a Lab environment.
Hardware Oriented Signal Processing 1
| Semester | 1 |
|---|---|
| Studienjahr | 1 |
| Lehrveranstaltungsnummer | AISM1HOSIL |
| Typ | IL |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 1 |
| ECTS-Punkte | 1,5 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Signal acquisition, sensors, signal amplifiers, digital-analog-converters (DACs), types of DACs, analog-digital-converters (ADCs), ADC-types and architectures, measurement devices and -systems, measurement and analysis of signal properties.
Übergeordnetes Modul:
Digital Signal Processing 1
Kompetenzerwerb aus dem übergeordneten Modul:
Students are able to 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 familiar with the foundations of signal sampling and discretisation and can apply important transformations, e.g. Fourier-, Laplace and z-transformation. They can transform continuous to discrete time systems e. g. with help of the impuls invariant or bilinear transformation and understand the restrictions. They understand basic algorithms in digital signal processing like FFT, convolution and correlation. They have profound knowledge in designing an implementing digital filters and are also familiar with their applications. They understand the operation of A/D and D/A converters and are able to simulate continuous and discrete time systems with standard tools. They have experience in measuring and analysing signal and system properties in a Lab environment.
Selected Topics in Mathematics and Modelling
| Semester | 1 |
|---|---|
| Studienjahr | 1 |
| Lehrveranstaltungsnummer | AISM1STMIL |
| Typ | IL |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 2 |
| ECTS-Punkte | 3 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Selected chapters from analysis (multidimensional differentiation and integration), algebra (vector spaces with inner product, eigenvalue theory, orthonormal bases, coordinate transformation, examples of vector spaces and applications), numerics (error analysis, conditioning and algorithmics) and Matlab (CLI and programming).
Übergeordnetes Modul:
Mathematics and Modelling
Kompetenzerwerb aus dem übergeordneten Modul:
On completion of the module, students are able to understand specialiced articles from the field of theoretical IT, to use mathematical modelling methods to solve real world problems and to translate those into respective algorithms. In particular, they have the ability to employ estimation theory and inferential statistics methods to analyse complex problems and they understand the mathematical basics of stochastic simulation. they are aware of the basic principles of Fourier theory and potential applications in signal and image processing and do have experience in solving corresponding exercises. They understand the relation between differential equations and correpsponding real world problems. Knowledge of theoretical basics and formal methods are rounded off by proficiency in working with mathematical software.
Signals and Systems 1
| Semester | 1 |
|---|---|
| Studienjahr | 1 |
| Lehrveranstaltungsnummer | AISM1SASIL |
| Typ | IL |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 2 |
| ECTS-Punkte | 2 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Basic signal and system properties, time and frequency domain, Fourier series and Fourier transformation (FT), FT of single impulses and periodic signals, power density spectrum (Parseval), convolution, convolution property, dirac impulse, dirac impulse sequence stochastic signals, variance and power of stochastic signals, autocorrelation and cross correlation sampling theorem, aliasing, zero order hold sampling, quantisation, quantisation error Laplace transformation, transfer function, pole zero map, discrete time signals and systems, z-transform, z-transfer function.
Übergeordnetes Modul:
Digital Signal Processing 1
Kompetenzerwerb aus dem übergeordneten Modul:
Students are able to 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 familiar with the foundations of signal sampling and discretisation and can apply important transformations, e.g. Fourier-, Laplace and z-transformation. They can transform continuous to discrete time systems e. g. with help of the impuls invariant or bilinear transformation and understand the restrictions. They understand basic algorithms in digital signal processing like FFT, convolution and correlation. They have profound knowledge in designing an implementing digital filters and are also familiar with their applications. They understand the operation of A/D and D/A converters and are able to simulate continuous and discrete time systems with standard tools. They have experience in measuring and analysing signal and system properties in a Lab environment.
Applied Statistics
| Semester | 2 |
|---|---|
| Studienjahr | 1 |
| Lehrveranstaltungsnummer | AISM2APSIL |
| Typ | IL |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 2 |
| ECTS-Punkte | 3 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Estimation theory: point and interval estimators, confidence intervals; application to stochastic simulations: random number generators, simulation model, result analysis; statistical test theory: comparison of mean values, significance, outlook ANOVA; application within datamining: preprocessing, feature extraction, outlook: PCA
Übergeordnetes Modul:
Mathematics and Modelling
Kompetenzerwerb aus dem übergeordneten Modul:
On completion of the module, students are able to understand specialiced articles from the field of theoretical IT, to use mathematical modelling methods to solve real world problems and to translate those into respective algorithms. In particular, they have the ability to employ estimation theory and inferential statistics methods to analyse complex problems and they understand the mathematical basics of stochastic simulation. they are aware of the basic principles of Fourier theory and potential applications in signal and image processing and do have experience in solving corresponding exercises. They understand the relation between differential equations and correpsponding real world problems. Knowledge of theoretical basics and formal methods are rounded off by proficiency in working with mathematical software.
Digital Signal Processing 2
| Semester | 2 |
|---|---|
| Studienjahr | 1 |
| Lehrveranstaltungsnummer | AISM2DSPPT |
| Typ | PT |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 1 |
| ECTS-Punkte | 2 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Solution of project based exercises related to the content of the lecture Digital Signal Processing 2.
Übergeordnetes Modul:
Digital Signal Processing 2
Kompetenzerwerb aus dem übergeordneten Modul:
Students know the principles of continuous filters and continuous control applications. They know details in digital filter design, e.g. advantages and disadvantages of different filter types and design methods. They understand the problem of quatisation errors of filter coefficients and how to design 2nd order sections IIR-filters. They know how to design special filters like notch filters or comb filters. They are able to implement digital filters in a standard programming language, e.g. in C. Students understand the concept of adaptive signal processing, e.g. adaptive LMS filter. They know how to design and implement simple digital controllers. They are familiar with the basics of digital data transmission. They understand the basics of 2D-signal processing and can design standard 2D-filters. They can solve complex signal processing problems on a given hardware platform.
Digital Signal Processing 2
| Semester | 2 |
|---|---|
| Studienjahr | 1 |
| Lehrveranstaltungsnummer | AISM2DSPIL |
| Typ | IL |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 3 |
| ECTS-Punkte | 4 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Filter structures, 2nd order section IIR filters, frequency transformations, special filters (notch filter, comb filter), implementation in Matlab and C principle and theory of adaptive FIR filters (LMS-filter) basics of digital controlling, simple digital controller (design and implementation) transmition of digital signals, 2D signal processing basics.
Übergeordnetes Modul:
Digital Signal Processing 2
Kompetenzerwerb aus dem übergeordneten Modul:
Students know the principles of continuous filters and continuous control applications. They know details in digital filter design, e.g. advantages and disadvantages of different filter types and design methods. They understand the problem of quatisation errors of filter coefficients and how to design 2nd order sections IIR-filters. They know how to design special filters like notch filters or comb filters. They are able to implement digital filters in a standard programming language, e.g. in C. Students understand the concept of adaptive signal processing, e.g. adaptive LMS filter. They know how to design and implement simple digital controllers. They are familiar with the basics of digital data transmission. They understand the basics of 2D-signal processing and can design standard 2D-filters. They can solve complex signal processing problems on a given hardware platform.
Signals and Systems 2
| Semester | 2 |
|---|---|
| Studienjahr | 1 |
| Lehrveranstaltungsnummer | AISM2SASIL |
| Typ | IL |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 2 |
| ECTS-Punkte | 2 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Stability examination of continuous and discrete systems (root locus), analog standard filters (Butterworth, Tschebyscheff, Cauer, Bessel), principle of controlling, control loop, design of simple controllers, pid controller, fuzzy logic.
Übergeordnetes Modul:
Digital Signal Processing 2
Kompetenzerwerb aus dem übergeordneten Modul:
Students know the principles of continuous filters and continuous control applications. They know details in digital filter design, e.g. advantages and disadvantages of different filter types and design methods. They understand the problem of quatisation errors of filter coefficients and how to design 2nd order sections IIR-filters. They know how to design special filters like notch filters or comb filters. They are able to implement digital filters in a standard programming language, e.g. in C. Students understand the concept of adaptive signal processing, e.g. adaptive LMS filter. They know how to design and implement simple digital controllers. They are familiar with the basics of digital data transmission. They understand the basics of 2D-signal processing and can design standard 2D-filters. They can solve complex signal processing problems on a given hardware platform.
Data Mining
| Semester | 3 |
|---|---|
| Studienjahr | 2 |
| Lehrveranstaltungsnummer | AISM3DMGIL |
| Typ | IL |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 2 |
| ECTS-Punkte | 2,5 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Object-relational database systems; data warehousing; data management in distributed systems; design of analytical information systems; integrated data management in an industrial environment; XML and databases; selected chapters from advanced database technologies (e.g. OLAP, information retrieval, knowledge discovery).
Übergeordnetes Modul:
Knowledge Discovery
Kompetenzerwerb aus dem übergeordneten Modul:
Building on database system basics, the students understand the theory and practice of information retrieval as well as information procurement from extensive datasets. They are familiar with the basics of data warehousing such as the integration and separation of data from distributed and differently structured data sets. The students are able to select and apply suitable data mining methods, i.e. implement statisticalmathematical methods for the detection of patterns and correlations in data. Furthermore, students are able to understand the theoretical framework for most methods in classical statistical pattern recognition and have gained in experience in using and applying classical methodologies in this area.
IT-Projectmanagement and Softwareprojects
| Semester | 3 |
|---|---|
| Studienjahr | 2 |
| Lehrveranstaltungsnummer | AISM3PMTIL |
| Typ | IL |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 2 |
| ECTS-Punkte | 3,5 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
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.
Übergeordnetes Modul:
Applied Sciences and Methods
Kompetenzerwerb aus dem übergeordneten Modul:
The students are able to independently identify and develop target-oriented research topics for scientific papers and to prepare those in the form of an exposé. They can argue logically and in line with scientific standards as well as understand the importance of a methodical approach. They are proficient in networked thinking and synthetic synopsis. They know the publication lifecycle including the review process. Furthermore, they are able to assess textual, formal and structural quality aspects of scientific papers. The students are also aware of the principles, methods and processes of IT-project management, also in virtual environments (eCollaboration, virtual Project Management - "vPM") like P2B (www.pool2business.eu) and the prerequisites for successful IT-innovations and applications.
Master Seminar 1
| Semester | 3 |
|---|---|
| Studienjahr | 2 |
| Lehrveranstaltungsnummer | AISM3MASSE |
| Typ | SE |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 1 |
| ECTS-Punkte | 1,5 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Systematic structuring of an exposé and its discursive defence in group situations; characteristics of a scientific working style; scientific publication cycle; introduction to philosophy of science and epistemology
Übergeordnetes Modul:
Applied Sciences and Methods
Kompetenzerwerb aus dem übergeordneten Modul:
The students are able to independently identify and develop target-oriented research topics for scientific papers and to prepare those in the form of an exposé. They can argue logically and in line with scientific standards as well as understand the importance of a methodical approach. They are proficient in networked thinking and synthetic synopsis. They know the publication lifecycle including the review process. Furthermore, they are able to assess textual, formal and structural quality aspects of scientific papers. The students are also aware of the principles, methods and processes of IT-project management, also in virtual environments (eCollaboration, virtual Project Management - "vPM") like P2B (www.pool2business.eu) and the prerequisites for successful IT-innovations and applications.
Pattern Recognition 1
| Semester | 3 |
|---|---|
| Studienjahr | 2 |
| Lehrveranstaltungsnummer | AISM3PRGIL |
| Typ | IL |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 2 |
| ECTS-Punkte | 2,5 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Pattern recognition process (data preprocessing, feature extraction, feature reduction, classification); training and testing methods, error types and performance analysis, min-risk and min-error, Baysian Decision Theory, PCA
Übergeordnetes Modul:
Knowledge Discovery
Kompetenzerwerb aus dem übergeordneten Modul:
Building on database system basics, the students understand the theory and practice of information retrieval as well as information procurement from extensive datasets. They are familiar with the basics of data warehousing such as the integration and separation of data from distributed and differently structured data sets. The students are able to select and apply suitable data mining methods, i.e. implement statisticalmathematical methods for the detection of patterns and correlations in data. Furthermore, students are able to understand the theoretical framework for most methods in classical statistical pattern recognition and have gained in experience in using and applying classical methodologies in this area.
EC: Ethics and Sustainability
| Semester | 3 |
|---|---|
| Studienjahr | 2 |
| Lehrveranstaltungsnummer | ITSM3ENHIL |
| Typ | IL |
| Art | Wahlpflicht |
| Unterrichtssprache | English |
| SWS | 1 |
| ECTS-Punkte | 1 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
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.
Übergeordnetes Modul:
Ethics and Sustainability
Kompetenzerwerb aus dem übergeordneten Modul:
Graduates are sensitised for dealing with moral and ethical issues (moral ideals, corporate pursuit of profit) in a technical/business context by means of concrete examples cases and are prepared for their practical implementation. They have a basic understanding of why ethically based disputes can be important for a company and the transfer of theoretical approaches to corporate decision-making processes and integration into practical, everyday company situations.
EC: Data Science 1 / Big Data Engineering
| Semester | 3 |
|---|---|
| Studienjahr | 2 |
| Lehrveranstaltungsnummer | AISM3DASIL |
| Typ | IL |
| Art | Wahlpflicht |
| Unterrichtssprache | English |
| SWS | 2 |
| ECTS-Punkte | 3 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Students know technical and organizational challenges imposed by big data applications and understand methods and algorithms for data-intensive software development. They are aware of the interdisciplinary aspects of big data engineering and have a basic command of established frameworks. As to particular methodology, students will be able to apply elementary matrix algebra to linear regression and extend that basic notion to the class of general linear models, multiple regression and nonlinear regression. They will develop the ability to independently apply basic regression techniques using R and to appreciate the underlying mathematical concepts. Paradigms and characteristics of big data computing; Architectural models for data-intensive applications; Overview of common big data frameworks; Concept overview of crowdsourcing, data fusion and data integration; Cloud-based infrastructures for data-intensive software development; Real-time delivery of results from big data analytics; Programming techniques for data-intensive applications, implementation of case studies; Selected topics in big data computing.
Übergeordnetes Modul:
Selected Topics in Applied Image and Signal Processing / Data Science
Kompetenzerwerb aus dem übergeordneten Modul:
On completion of the module, students are able to apply their knowledge gained in more general courses to specific application areas and will learn to select the most appropriate techniques and methods in actual, application oriented fields.
EC: Medical Imaging
| Semester | 3 |
|---|---|
| Studienjahr | 2 |
| Lehrveranstaltungsnummer | AISM3MIGIL |
| Typ | IL |
| Art | Wahlpflicht |
| Unterrichtssprache | English |
| SWS | 3 |
| ECTS-Punkte | 5 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
On completion of the course students are able to understand basics of different medical imaging modalities and their application in a clinical enviroment. Furthermore knowledge on basic anatomy and image representation as well as on methods dealing with advanced segmentation and registration of 4d data and 3D model rendering is given in a medical context. Students will also be able to apply their knowledge gained from prior courses onto medical images for the purpose of analysis, visualisation and diagnostics.
Übergeordnetes Modul:
Selected Topics in Applied Image and Signal Processing / Medical Imaging
Kompetenzerwerb aus dem übergeordneten Modul:
On completion of the module, students are able to apply their knowledge gained in more general courses to specific application areas and will learn to select the most appropriate techniques and methods in actual, application oriented fields.
EC: Platform Specific Signal Processing
| Semester | 3 |
|---|---|
| Studienjahr | 2 |
| Lehrveranstaltungsnummer | AISM3PSSIL |
| Typ | IL |
| Art | Wahlpflicht |
| Unterrichtssprache | English |
| SWS | 3 |
| ECTS-Punkte | 5 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Functional principle of modern signal processors, fixed point formats, special hardware architectures (FPGAs), modern development environments and simulation tools, speed optimization of signal processing algorithms, parallelisation in software and hardware, hardware description languages (VHDL).
Übergeordnetes Modul:
Selected Topics in Applied Image and Signal Processing / Platform Specific
Kompetenzerwerb aus dem übergeordneten Modul:
On completion of the module, students are able to apply their knowledge gained in more general courses to specific application areas and will learn to select the most appropriate techniques and methods in actual, application oriented fields.
Master Exam
| Semester | 4 |
|---|---|
| Studienjahr | 2 |
| Lehrveranstaltungsnummer | AISM4MEXDP |
| Typ | DP |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 0 |
| ECTS-Punkte | 0 |
| Prüfungscharakter | abschließend |
Lehrveranstaltungsinhalte:
-
Übergeordnetes Modul:
Master Thesis
Kompetenzerwerb aus dem übergeordneten Modul:
The students are able to independently write sound academic papers based on common international standards. They can proceed methodically and systematically. They can analyse and present problems, provide solutions as well as formulate these appropriately and critically scrutinise them. The students are able to defend their approach.
Master Seminar 2
| Semester | 4 |
|---|---|
| Studienjahr | 2 |
| Lehrveranstaltungsnummer | AISM4MASSE |
| Typ | SE |
| Art | Pflicht |
| Unterrichtssprache | English |
| SWS | 1 |
| ECTS-Punkte | 2 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
Discursive defence of parts of the master thesis in group situations; presentation of scientific work.
Übergeordnetes Modul:
Applied Sciences and Methods
Kompetenzerwerb aus dem übergeordneten Modul:
The students are able to independently identify and develop target-oriented research topics for scientific papers and to prepare those in the form of an exposé. They can argue logically and in line with scientific standards as well as understand the importance of a methodical approach. They are proficient in networked thinking and synthetic synopsis. They know the publication lifecycle including the review process. Furthermore, they are able to assess textual, formal and structural quality aspects of scientific papers. The students are also aware of the principles, methods and processes of IT-project management, also in virtual environments (eCollaboration, virtual Project Management - "vPM") like P2B (www.pool2business.eu) and the prerequisites for successful IT-innovations and applications.
Master Thesis
| Semester | 4 |
|---|---|
| Studienjahr | 2 |
| Lehrveranstaltungsnummer | AISM4MTHIT |
| Typ | IT |
| Art | Diplom/Masterarbeit |
| Unterrichtssprache | English |
| SWS | 0 |
| ECTS-Punkte | 28 |
| Prüfungscharakter | immanent |
Lehrveranstaltungsinhalte:
-
Übergeordnetes Modul:
Master Thesis
Kompetenzerwerb aus dem übergeordneten Modul:
The students are able to independently write sound academic papers based on common international standards. They can proceed methodically and systematically. They can analyse and present problems, provide solutions as well as formulate these appropriately and critically scrutinise them. The students are able to defend their approach.
| Legend | |
| Semester | Semesters 1, 3, 5: courses held only in winter semester (mid-September to end of January), Semesters 2, 4, 6: courses held only in summer semester (mid-February to end of June) |
| SWS | weekly contact hours over 14 weeks in semester (example SWS 2 equals 28 contact hours for the whole course |
| ECTS Credits | Work load in ECTS credits, 1 ECTS credit equals an estimated 25 hours of work for the student |
| INTL-Code | Indicates categories for incoming students 5: offered in English on a routine basis 4: offered in English if a specified number of incoming students attend (usually 3) 3: taught in German but support material in English, exams can also be taken in English, active support from a student buddy 2: taught in German, incoming students require sufficient German proficiency to follow class 1: not available for incomings |
| Type | BP = Bachelor final exam DP/MP = Master final exam IL = Lecture with integrated project work IT = Individual training/phases LB = Lab (session) PS = Pro-seminar PT = Project RC = Course with integrated reflective practice RE = Revision course SE = Seminar TU = Tutorial UB = Practice session/Subject practical sessions VO = Lecture |