Full-Time

Information Technology and Systems Management

Course titleSWSECTSTYPE

Agile Project Management

Semester 1
Academic year 1
Course code ITSM1APMIL
Type IL
Kind Compulsory
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Agile management and control of projects and processes; process models in software development such as Scrum, KANBAN or "Extreme Programming". Scrum, KANBAN or "Extreme Programming"; "minimal viable product"; agile basic principles - value driven delivery, self-organising teams, simple & focused communication; digitalisation & agility interdependencies; the development stages towards an agile organisation; Agile software development as a driver in the software development process, which should increase transparency and the speed of change and lead to faster deployment of the developed system in order to minimise risks and undesirable developments in the development process; classic, hybrid or agile process models for decision-making; agile frameworks for building agile organisations - e.g. a comparison of SAFe, Agile and Agile. e.g. a comparison of SAFe, LESS, Nexus, scrum @ scale; project management and software engineering skills are to be applied in practical implementation; minimal viable product - a model for the market-adequate "just in time" development of digital products; project organisation (process-oriented and agile procedure models, roles, work packages, milestones, reporting, results). Project implementation is carried out with templates from software engineering for the development, documentation and communication of software architectures using tools such as ARC42; agile implementation explained using Scrum as an example; methodological alternatives to Scrum; the state of agility in Austria and Europe. Certification as a Scrum Master is possible.

Learning Outcomes:

Alumni will learn the necessary theoretical and practical agile project management and software engineering skills - based on the practical implementation of a continuous software engineering - R&D project - which is to be completed in the 2nd and 3rd semester to be completed.

Superior module:

Projektmanagement und Individualkompetenz

Module description:

xxx

Ethics & Sustainability

Semester 1
Academic year 1
Course code ITSM1EUNIL
Type IL
Kind Compulsory
Language of instruction German
SWS 1
ECTS Credits 2
Examination character immanent

Lecture content:

Never before has the need for (professional) ethical orientation been so great as in the last decade. At the same time, we are currently encountering ethics in the most diverse forms and in the most diverse hyphen variants: Bioethics, medical ethics, animal ethics, ethics and politics, ethics and economics, ethics lessons instead of religious instruction at schools, from addressee ethics to environmental ethics, from everyday ethics to system ethics. Our existence seems to be moving in ethically and morally charged times, especially because the concepts of ethics and sustainability themselves are being used in an increasingly blurred and inflationary way. The symposium therefore tries to contribute to clearing the jungle of terms and to raise awareness for (professional) ethical questions and questions about sustainability.

Learning Outcomes:

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

Superior module:

Sozialkompetenz und Kommunikation 1

Module description:

xxx

Informatics & Cloud Technologies

Semester 1
Academic year 1
Course code ITSM1ICTIL
Type IL
Kind Compulsory
Language of instruction German
SWS 3
ECTS Credits 4
Examination character immanent

Lecture content:

Application-related communication paradigms; cross-platform protocols and services as well as distributed data management; overview of component technologies; development, integration and deployment paradigms for distributed software systems; cloud service models; communication techniques for time-dependent data streams; current topics and application examples of software technologies.

Learning Outcomes:

Alumni design, implement and deploy distributed software systems and realise distributed data management and distributed software-based services. They use current component technologies and business-relevant middleware and cloud systems and use use methods and tools of platform-independent software development.

Superior module:

Informatik- und Softwaretechnologien

Module description:

xxx

Intercultural Communication Skills

Semester 1
Academic year 1
Course code ITSM1ICSIL
Type IL
Kind Compulsory
Language of instruction English
SWS 1
ECTS Credits 2
Examination character immanent

Lecture content:

Basics of perception psychology relevant to intercultural communication, definition of intercultural interaction and communication competence, interaction traps, practical application through interaction games and exercises.

Learning Outcomes:

Alumni are able to identify the complex factors that influence communication in intercultural contexts. They are able to classify their own culturally determined role in the communication context.

Superior module:

Sozialkompetenz und Kommunikation 1

Module description:

xxx

Sales and Marketing

Semester 1
Academic year 1
Course code ITSM1VUMIL
Type IL
Kind Compulsory
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

The aim of this course is to understand market research methods and their areas of application, marketing mix, product policy, brand policy, pricing policy, sales policy, sales management, key account management, how to achieve customer orientation and customer loyalty, understanding the sales cycle, sales channels and multi-channel management, national and international sales strategies, communication policy, internet marketing, life cycle marketing, sales controlling and planning. Practical implementation through case studies, immersion in sales and marketing thinking ("Sales and Marketing Strategies") and its implementation, analysis of best practice examples. Environmental factors, market entry strategies, cultures and their influences on marketing strategy and intercultural negotiations; consideration of ethics in global marketing.

Learning Outcomes:

The alumni know the essential basic terms of "sales and marketing" and their practical meaning. They have an overview of the tools of the marketing mix. The alumni know the basics of essential marketing theories and their practical use in specific situations. They know the most important modern trends in marketing and their effect on the success of a company. The alumni understand sales mechanisms in an economic context and can design them themselves. They work on a complex task from different areas of the economy (case studies), independently solve a problem and document it in an engineering manner.

Superior module:

Projektmanagement und Individualkompetenz

Module description:

xxx

Software and Process Notations

Semester 1
Academic year 1
Course code ITSM1SPNIL
Type IL
Kind Compulsory
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Textual and graphical notations for software development and process modelling (e.g. BPMN, SPEM); notations for service and interface specifications; use of common notation tools; use of domain-specific UML profiles; meta-modelling; current topics in software notations.

Learning Outcomes:

Alumni develop formalised descriptions of different software development artefacts as well as economic flows and networked processes. They use the common UML diagram types for system development and extend the notation, for example, by forming profiles. You use appropriate CASE tools and evaluate methods and tools of platform-independent software development. You master abstraction concepts of model-driven software development.

Superior module:

Informatik- und Softwaretechnologien

Module description:

xxx

SP: Cyber Security 1

SP: Foundations of IT-Security

Semester 1
Academic year 1
Course code ITSM1FISIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

After a brief review of the basics of cryptography, IT security is presented as an overall area. Topics such as the analysis and presentation of current threats in IT are discussed as well as organisational aspects of IT security, i.e. the embedding and implementation of a security strategy in the company environment. Subsequently, the planning and implementation of a systematic security analysis of complex IT systems will be discussed, the practical implementation of which will also be demonstrated in the laboratory. Finally, advanced topics of IT security such as intrusion detection and prevention are discussed in detail.

Learning Outcomes:

The alumni acquire knowledge and practical skills in the field of operation and design of extended, secured communication networks. They understand potential threats to network infrastructures and know countermeasures. The alumni are able to practically implement countermeasures against current threats.

Superior module:

SP: Cyber Security 1

Module description:

xxx

SP: Network Reliability and Virtualization

Semester 1
Academic year 1
Course code ITSM1NRZIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Network planning and implementation with a focus on network reliability. Advanced network topics from the area of ISP and data centre networking, such as the Border Gateway Protocol (BGP), multicast, Virtual Extensible Lan (VxLAN), storage networks, etc. Virtualisation and its impact on modern networks. Current developments in networking such as network virtualisation, software defined networks (SDN), programmable dataplanes (e.g. P4) and next-gen SDN.

Learning Outcomes:

The alumni can plan and implement reliable, high-performance IP networks, they can evaluate and optimise networks with regard to their reliability. They can plan, implement and optimise IP multicast networks. They are fundamentally familiar with BGP and can carry out basic BGP configurations. You are familiar with current network technologies from the areas of enterprise networking, data centre networking and service provider networking. You have insight into current developments in the field of network technology (e.g. Software Defined Networks (SDN), Programmable Dataplanes (e.g. P4) and Next-Gen SDN).

Superior module:

SP: Cyber Security 1

Module description:

xxx

SP: Cyber Security Supplement

SP: Legal aspects of Cyber Security

Semester 1
Academic year 1
Course code ITSM1RACIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 2
Examination character immanent

Lecture content:

The contents of the course are in particular data protection law, criminal law with a focus on computer criminal law and association liability as well as civil liability issues, in each case including the relevant procedural provisions. In addition, the subject area of compliance, selected aspects of labour law and corporate law, as well as cyber insurance and special special legal foundations (e.g. the NISG) are dealt with.

Learning Outcomes:

Alumni know the relevant legal principles from data protection, criminal, civil, labour and company law as well as from the areas of compliance and insurance, can apply these to concrete cases and derive implications from them. They know the legal basis for prevention and the legal consequences of cyber security incidents for the different actors.

Superior module:

SP: Cyber Security Supplement

Module description:

xxx

SP: Social Engineering

Semester 1
Academic year 1
Course code ITSM1SEGIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Basics of human psychology with a focus on trust and behaviour in stressful situations; common techniques of social engineering and their target groups; concepts for prevention and defence against social engineering; processing of social engineering incidents and communication with those affected

Learning Outcomes:

Alumni will understand how and why social engineering works, which circumstances and how the probability of success of social engineering can be minimised. Furthermore, the alumni are able to identify and defend against common social engineering techniques. They will also be able to inform third parties, especially those not familiar with the subject, about social engineering and its prevention.

Superior module:

SP: Cyber Security Supplement

Module description:

xxx

SP: Data Science & Analytics 1

SP: Analytics and Knowledge Discovery

Semester 1
Academic year 1
Course code ITSM1AKDIL
Type IL
Kind Elective
Language of instruction English
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Approaches to Data Analysis, EDA Parallel Lines, Boxplots, Kernel Density Estimators, Basic Coding and Embedding of Data, Curse of Dimensionality, PCA, t-SNE, K-means, Hierarchical Clustering, Spectral Clustering, Distances and Similarity Measures. (Applications to data analysis, Exploratory data analysis, Parallel lines and boxplots, Kernel density estimators, Basic coding of data, Curse of dimensionality, Principal component analysis, t-SNE, k-means, Hierarchical clustering, Spectral clustering, Distances and similarity measures).

Learning Outcomes:

Alumni are able to apply classical methods of explorative data analysis to different types of data (numerical, categorical, textual). They are able to implement a knowledge discovery process (data mining, information retrieval, structure discovery methods), reduce the dimensionality of the data, identify clusters and visualise them accordingly. The course focuses on non-supervised learning.

Superior module:

SP: Data Science & Analytics 1

Module description:

xxx

SP: Data Science

Semester 1
Academic year 1
Course code ITSM1DCEIL
Type IL
Kind Elective
Language of instruction English
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Terminology, Design Cycle and Extended Design Cycle, Data Sampling and Normalisation, Performance Measures, Cross Validation, Training Policies, K-nearest Neighbour and Minimum Distance Classifier, Natural Language Processing and Specific Features, Low Level Image Features. Features.

Learning Outcomes:

The alumni know types and components of Data Science projects, can describe their describe their structure and name the corresponding positions and designations of employees. designate. They understand the concepts behind data, models and algorithms and use technical language to describe them. technical language to describe them. They discuss the suitability of data collections or data acquisition processes for specific tasks. They are able to apply methods and algorithms to extract information from data in different representations (numerical, categorical, one-hot or textual). They know methods to collect, clean and visualise data to develop an understanding from an application perspective. Following the further design cycle for supervised learning, they can implement feature extraction and sampling of training and test data, parameterise and train selected (simple) classifiers and evaluate their performance. For this purpose, they use state-of-the-art development environments and scalable technologies and are able to argue the content of selected solutions.

Superior module:

SP: Data Science & Analytics 1

Module description:

xxx

SP: Mathematics and Modelling

SP: Mathematics and Modelling

Semester 1
Academic year 1
Course code ITSM1MAMIL
Type IL
Kind Elective
Language of instruction German
SWS 4
ECTS Credits 5
Examination character immanent

Lecture content:

Vector-valued functions in several variables, vector fields, scalar fields, partial derivatives, gradient operator, Jacobian and Hessian matrices, directional derivatives, Taylor series in multiple variables, critical points, local minima, maxima and saddle points, convex optimisation and and applications. Integral calculus, Pre-Hilbert space, inner product, (orthonormal) base and base transformation, eigenvalues, eigenvectors, matrix decompositions and applications. (PCA).

Learning Outcomes:

The alumni can model facts with multidimensional functions. They are able to analyse the change behaviour of these functions and determine critical points. They can approximate complex functions with multidimensional polynomials (in particular with tangential planes and second-order Taylor polynomials). They are able to use gradient-based methods to find local minima. They understand selected problems of convex optimisation and can solve them with mathematical software. Alumni are able to calculate the most important matrix decompositions and apply eigenvalue theory to perform the principal axis transformation for data. Alumni are able to solve multidimensional integrals. They understand the construct of a vector space (VR) with inner product and are able to identify it in different application areas. They master the coordinate transformation for the change of a basis in finite-dimensional VRs and know the connection with Fourier analysis. They know selected areas of application of the methods mentioned.

Superior module:

SP: Mathematics and Modelling

Module description:

--

SP: Smart Systems & Robotics 1

SP: Digital Signal Processing 1

Semester 1
Academic year 1
Course code ITSM1DSPIL
Type IL
Kind Elective
Language of instruction English
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Theory of discrete signals and systems, discrete Zourier transform, FFT, power density spectrum, discrete convolution and correlation, fast variants with FFT, interpolation, calculations in the z-plane, z-transfer function, stability and frequency response of discrete systems, discretisation of continuous systems (bilinear transform, pulse-invariant transformation), digital filters, principle of FIR and IIR filters, FIR filter types, FIR filter design, fast FIR filters with FFT, frequency transformations, simulation of signal processing algorithms and implementation of discrete systems in a laboratory environment (e. g. Matlab, Python). g. Matlab, Python, C).

Learning Outcomes:

Alumni understand the mathematical concepts for the description of continuous and discrete signals and systems and know the relationships between time and frequency domain. They are familiar with the basics of sampling and can apply basic transformations (Fourier, Laplace, z). They understand the basic algorithms such as FFT, discrete convolution and correlation. They can transform continuous systems into discrete ones and understand the constraints involved. You have a sound knowledge in the design and implementation of digital FIR filters and understand their possible applications. They have experience in simulating DSP algorithms in a laboratory environment and can implement discrete systems using simulation software and low-level programming languages.

Superior module:

SP: Smart Systems & Robotics 1

Module description:

xxx

SP: Robotics 1

Semester 1
Academic year 1
Course code ITSM1IRKIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Schematic drawing of a robot, basics of position description, rotation matrices, parameterisation of rotations, combination of rotation and displacement, homogeneous transformation matrices, DH convention for industrial robots, forward transformation, backward transformation of serial kinematics (geometric, algebraic and numerical methods), velocity kinematics, trajectory planning.

Learning Outcomes:

The alumni can describe a robot position with the help of a schematic robot drawing. They can calculate the transformations (position and orientation) and the velocities between tool and axis coordinates. They are familiar with the common procedures of trajectory planning and can plan trajectories for robots. You can implement robot programs in a robot simulator and analyse simulation runs.

Superior module:

SP: Smart Systems & Robotics 1

Module description:

xxx

Course titleSWSECTSTYPE

Applied Statistics

Semester 2
Academic year 1
Course code ITSM2AWSIL
Type IL
Kind Compulsory
Language of instruction German
SWS 3
ECTS Credits 4
Examination character immanent

Lecture content:

Estimation theory: point and interval estimators, maximum likelihood method, method of moments, parametric and non-parametric models (kernel density estimators, normal distributions, mixed models), statistical tests, study design and analysis of variance. Data Visualisation. Outlook: Random numbers and randomisation; Graphical models and applications.

Learning Outcomes:

Alumni can apply methods of inferential statistics to data and communicate the results obtained linguistically and graphically. They can describe and visualise data with models and are able to represent dependencies of random variables through graphical models. They know statistical standards and are able to plan, conduct and document experiments. They know applications of random number generators in the field of generative models and can generate corresponding data with mathematical software.

Superior module:

Mathematische Methoden

Module description:

xxx

Discussion and Argumentation Skills

Semester 2
Academic year 1
Course code ITSM2DASIL
Type IL
Kind Compulsory
Language of instruction English
SWS 1
ECTS Credits 2
Examination character immanent

Lecture content:

Argumentation, negotiation and discussion techniques, use of appropriate phrases and rhetorical devices, practical examples and role plays. The contents of the course are coordinated with the teamwork in the R&D project.

Learning Outcomes:

Alumni are able to present a topic in English clearly and comprehensibly. They are able to build up arguments logically and stringently and to respond to questions and counter-arguments linguistically competently. Application of the learning content is reflected in the reflection and development of suitable argumentation chains in the context of the R&D project.

Superior module:

Sozialkompetenz und Kommunikation 1

Module description:

xxx

IT- & Security Management

Semester 2
Academic year 1
Course code ITSM2ITSIL
Type IL
Kind Compulsory
Language of instruction German
SWS 2
ECTS Credits 3
Examination character final

Lecture content:

IT management & enterprise architecture; IT planning and control (= IT strategies); IT portfolio; service level management, strategic IT controlling; resource planning and capacity management; TCO analysis; procurement management; IT in/outsourcing; basic IT governance (Sarbanes-Oxley Act, BASEL III), ITIL, IT financial management, ISO17799 and ISO20000, change management, problem management (helpdesk), security management, lifecycle management, disaster recovery measures; backup restore plans. Risk management, information and data classification; patent law. Emerging & disruptive technologies; Hybrid & distributed cloud & cloud operations; Automation in software development (DevSecOps). IT security organisation, governance, risk & compliance; security standards and frameworks (ISO27000, IT-Grundschutz), cyber security strategies, security life cycle, security policies/standards guidelines/procedures, access control models, cloud security, industrial control systems (ICS), ethical hacking and penetration testing, IT & malware forensics, incident handling & computer emergency response team (CERT), legal principles / legal specifics; Cryptographic Algorithms & Protocols, Security Protocols in Practice, Security Engineering, Security Assessment; Security Policies, Critical Infrastructure & Essential Services Providers NIS), Types of Attack, Security Mechanisms (Firewalls, Encryption, Authentication, Logging); Data Privacy (DSGVO)&Protection; Security Target Operating Model; Incident Response & Cisis Management; Security Certifications (ISO27001), Regulations (Cyber Security Act) & Compliance; Security Target Operating Model.

Learning Outcomes:

The alumni have the necessary knowledge to help shape IT in a company and to implement specific IT management tasks. As future IT & security managers, they understand the relevant operational, legal and social environment and master the structure, management (roles/access rights) of an IT infrastructure in order to comply with the EU Data Protection Regulation, among other things. They have the ability to align IT with the company organisation and needs and understand IT as part of the operational processes. Furthermore, they can manage IT as a business and enable an improvement of core business processes through innovative technologies (Technology Business Management / CTO). Equally in-depth is the acquisition of knowledge in the area of security management and dealing with changing threats and their impact on the cyber security strategy of companies. Alumni are familiar with the cryptographic basics of IT security. They can assess security threats and know current countermeasures. The alumni can also implement technical measures for IT security independently and competently.

Superior module:

IT-Management und Innovation

Module description:

xxx

R&D Project 1

Semester 2
Academic year 1
Course code ITSM2RDPPT
Type PT
Kind Compulsory
Language of instruction German
SWS 4
ECTS Credits 4
Examination character immanent

Lecture content:

Research and development-oriented project work with technical-methodical processing of topics of the specialisations. Focus on analysis, evaluation and selection of applied methods and technologies. Practical implementation of technical projects, partly in cooperation or coordination with commercial enterprises. Accompanying project management, reflection and coaching on teamwork as well as preparation and target group-oriented communication of the project results.

Learning Outcomes:

The alumni are able to plan, process and present a research and development-oriented project work in a team. They find independent solutions in the context of scientific evidence (research competence as well as corresponding evidence) and acquire practice-oriented problem-solving competence. This sensitises them to those areas that require in-depth, self-directed knowledge acquisition carried out in a team.

Superior module:

Projekt und Workshop 1

Module description:

xxx

Selected Topics Industrial Informatics 1

Semester 2
Academic year 1
Course code ITSM2AKIIL
Type IL
Kind Compulsory
Language of instruction German
SWS 1
ECTS Credits 2
Examination character immanent

Lecture content:

Acquisition of a basic understanding of a selected topic of a basic technology from the field of "Industrial Informatics"; reflection and critical discussion on the topic in connection with social issues (economic, ecological, social, etc.). Possible topics: OPC Unified Architecture, Mobile & Pervasive (Business) Applications, Advanced Service Engineering, Artificial Intelligence, Systems Engineering, etc. Announcement of the topic focus at the end of the 1st semester. Instead of this LVA, two alternatives are possible, which are credited in the course of an expression of interest and self-organised learning (within the framework of 4 ECTS / 2 SWS): Alternative 1: Short Term Mobility: exchange programme at a partner university, time window between 2nd and 3rd semester Alternative 2: Organisation Laboratory: cooperation with the University of Klagenfurt, block event 2nd semester

Learning Outcomes:

The alumni get to know a new basic technology ("State of the Art Method") from the field of "Industrial Informatics", which has not been taught much in the study programme so far. They are able to describe and differentiate between complex technical contents and assign them to possible areas of application. At the same time, they can reflect and critically question their effects on social issues.

Superior module:

Projekt und Workshop 1

Module description:

xxx

Software und Systems Engineering

Semester 2
Academic year 1
Course code ITSM2SSEIL
Type IL
Kind Compulsory
Language of instruction German
SWS 3
ECTS Credits 4
Examination character immanent

Lecture content:

Software project management; SW quality management; effort estimation techniques; selection and use of product and process metrics; SW risk management; 'Development and IT Operations' (DevOps) challenges and strategies; application security and incident management; software engineering techniques for software development 'at large'; software engineering tool chain; current issues in software engineering.

Learning Outcomes:

The alumni understand the various task fields and activities within the framework of the software development process (requirements engineering & software quality; software architecture; detailed design & design-for-X; verification and validation) and the productive operation of software and systematically master the challenges of organising different economically relevant software projects. The alumni assess process models and develop them further independently and independently drive forward the conception, implementation and monitoring of professional software projects and the associated productive operation.

Superior module:

Software Engineering

Module description:

xxx

SP: Adaptive Software Architecture

SP: Adaptive Software Architecture

Semester 2
Academic year 1
Course code ITSM2ASAIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Fundamentals and characterisation of software architectures; Architecture-related quality attributes; Model- and Service-Oriented Architectures; Evolutionary Software Architecture and emergent design; software architecture development and system integration; strategies and Integration of Heterogeneous Systems; Reference Architectures and Enterprise Integration Patterns'; software architecture assessment and architecture metrics; architecture documentation; Current topics on software architectures.

Learning Outcomes:

Alumni evaluate contemporary software architectures and can soundly argue architectural decisions for development and integration projects. They apply software design patterns as well as architecture patterns (especially enterprise integration patterns) and make informatic abstraction methods comprehensible and usable for involved stakeholders. They recognise innovation-relevant issues and independently develop suitable adaptive solution concepts to systematically and flexibly manage a high degree of technical-methodological heterogeneity.

Superior module:

SP: Adaptive Software Architecture

Module description:

xxx

SP: Cyber Security 1

SP: Privacy Enhancing Technologies

Semester 2
Academic year 1
Course code ITSM2PETIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Definitions of privacy in the technological context, goals and tasks of privacy-preserving technologies, differentiation from security; privacy-preserving protocols and their cryptographic building blocks, cryptographic basics of the procedures and protocols; application of homomorphic encryption, masking, differential privacy, etc.; design of own protocols and analysis of existing procedures, evaluation of procedures with regard to costs, benefits and attack possibilities; basics of formal proof.

Learning Outcomes:

The alumni can formulate the benefits and objectives of privacy-preserving technologies and assess them in the context of real problems. They can estimate the costs and benefits of applying such technologies and participate in the conception, design and selection of suitable solutions. The alumni have an overview of common procedures in selected domains, understand the formal and mathematical foundations of these and can assess these procedures with regard to their suitability, security and applicability. Furthermore, the alumni can also formally evaluate the functioning of simple protocols based on proven cryptographic procedures and plan and assess their costs and benefits as well as the risks when used in a real environment.

Superior module:

SP: Cyber Security 1

Module description:

xxx

SP: Secure Network Operations and Analytics

Semester 2
Academic year 1
Course code ITSM2SNOIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

The main content of this course is the management and organisation of IT security, as well as the secure operation of large network infrastructures. Organisational aspects of security management as well as technical aspects are covered. Current approaches to the organisational implementation of IT security in companies are covered as well as technical methods to collect relevant data on security and performance in the network and to analyse and process them in a meaningful way.

Learning Outcomes:

The alumni are familiar with current approaches to the organisational integration and management of IT security. They are familiar with the process for the creation of security policies and know procedures to ensure compliance with them, as well as the guarantee of secure operation through Security Information and Event Management (SIEM). The alumni know current procedures for implementing security concepts in large network infrastructures. They also know how to verify the secure operation of these infrastructures by collecting data on security and performance. data on security and performance and can apply advanced methods to evaluate and analyse this data. to evaluate and analyse this data.

Superior module:

SP: Cyber Security 1

Module description:

xxx

SP: Selected algorithms and optimisation

Semester 2
Academic year 1
Course code ITSM2AAOIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Theoretical and experimental methods and criteria for evaluating the efficiency of algorithms and data structures. Analysis of selected algorithms and data structures with reference to different application domains. Optimisation possibilities of algorithmic implementations. Selected techniques and informatic implementations of mathematical optimisation methods with reference to different application domains.

Learning Outcomes:

Alumni are able to select algorithms in their respective areas of application and evaluate them with regard to their resource requirements. They understand which optimisations are possible and expedient. Alumni are also able to carry out optimisations themselves and evaluate their success quantitatively.

Superior module:

SP: Cyber Security 1

Module description:

xxx

SP: Data Science & Analytics 1

SP: Machine Learning

Semester 2
Academic year 1
Course code ITSM2MLGIL
Type IL
Kind Elective
Language of instruction English
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Statistical learning theory, no-free-lunch theorem, learning curves, error functions, bias and variance; selected models: Maximum Entropy (Logistic Regression), Artificial Neural Networks, SVM (Kernel SVM, Multi-Class SVM, OneClass SVM), Naive Bayes, Minimum Risk.

Learning Outcomes:

Alumni understand the consequences and limitations of choosing a particular machine learning model in the context of statistical learning theory and in relation to the no-free-lunch theorem. They are able to select appropriately from known algorithms, parameterise them and evaluate them with respect to their complexity. During the During the training process, they can recognise overfitting and underfitting and counteract them with suitable countermeasures. They have the knowledge to select suitable machine learning models for different types of data (numerical, texts, images) and tasks (classification, representation learning, object recognition).

Superior module:

SP: Data Science & Analytics 1

Module description:

xxx

SP: Robust and Explainable AI

Semester 2
Academic year 1
Course code ITSM2REAIL
Type IL
Kind Elective
Language of instruction English
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Metrics for interpretability, comprehensibility and fairness of models; feature selection; model pruning; decision trees; ensemble methods; sensitivity analysis.

Learning Outcomes:

Alumni deal with explainable and interpretable artificial intelligence (XAI) models and can apply decision trees and their extensions as a form of them. This enables them to build robust systems whose predictions and decisions are comprehensible. The alumni understand how to interpret the influence of individual features on the result and communicate the model decisions. Furthermore, they are able to optimise the models in terms of their resource consumption through appropriate feature selection and/or model thinning. while keeping the prediction quality high. They can analyse the impact of unbalanced, biased or noisy data on trained systems in terms of fairness or robustness.

Superior module:

SP: Data Science & Analytics 1

Module description:

xxx

SP: Smart Systems & Robotics 1

SP: Digital Signal Processing 2

Semester 2
Academic year 1
Course code ITSM2DSPIL
Type IL
Kind Elective
Language of instruction English
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

IIR filter structures, IIR filter types, design of IIR filters, frequency transformations of IIR filters, quantisation problems with higher order filters, cascaded SoS filters, notch filters, comb filters, median filters, theory and implementation of adaptive FIR filters (LMS), polyphase filters, Quality improvement through oversampling, theory and simulation of a sigma-delta ADC, basics of 2D signal processing (image processing), application of standard SP algorithms for 2D signals, simulation and implementation of digital filters in a laboratory environment (e. g. Matlab, Python). g. Matlab, Python, C).

Learning Outcomes:

The alumni understand the theoretical basics of the design of IIR filters and know the advantages and disadvantages of different filter types and design methods. They understand the problem of coefficient quantisation and can implement IIR filters using cascaded SoS filter structures. They know the principle of notch, comb and median filters. The alumni understand the theory of adaptive LMS filters and can also implement them. They also know the theoretical principles of a sigma-delta ADC and can verify these in practice. They understand the application of standard DSP algorithms also for 2D signals (images).

Superior module:

SP: Smart Systems & Robotics 1

Module description:

xxx

SP: Robotics 2

Semester 2
Academic year 1
Course code ITSM2IRKIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Dynamics of industrial robots, Euler-Lagrange formalism, kinetic and potential energy, dynamic equations of motion of an industrial robot, Euler-Newton equations, single-axis control (setpoint sequence and disturbance rejection, feedforward control, anti-wind-up, state-space control, observer design), multi-variable control, inverse dynamics.

Learning Outcomes:

The alumni understand a dynamic multibody system. They can relate a multibody system and its governing variables to common robot kinematics and analyse them with suitable simulation systems. Based on the dynamic robot equations, the alumni can synthesise and evaluate multivariable controllers.

Superior module:

SP: Smart Systems & Robotics 1

Module description:

xxx

Course titleSWSECTSTYPE

Advanced Presentation Skills

Semester 3
Academic year 2
Course code ITSM3APSIL
Type IL
Kind Compulsory
Language of instruction English
SWS 1
ECTS Credits 2
Examination character immanent

Lecture content:

Summarising relevant technical texts and scientific articles and preparing them for oral presentation, target group-oriented and emotionally appealing presentation techniques, use of rhetorical devices, storyboarding and storytelling. The contents of the course are coordinated with the teamwork in the R&D project.

Learning Outcomes:

Alumni are able to present a topic in English in a clear and comprehensible way, using rhetorical devices as well as elements of storytelling according to the target group. They are able to apply the technique of storyboarding in the preparation of a presentation. Application of the learning content is reflected in the reflection and implementation of appropriate presentation techniques in the context of the R&D project.

Superior module:

Sozialkompetenz und Kommunikation 2

Module description:

xxx

Digital Innovation

Semester 3
Academic year 2
Course code ITSM3DINIL
Type IL
Kind Compulsory
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

The aim of this course is to understand how digitalisation in innovation management influences the development of new business models and the process of innovation development and management. Approaches such as design thinking or design innovation are covered. The students understand the complex challenge of working across divisions and can verify potential for "change" within the company. Through risk management, corporate risks are analysed using exemplary examples. Adaptation of existing company and work structures; assessment of digital transformations on the basis of economic, organisational and strategic procedures as well as processes (e.g. consideration of BPMN). Discussion of the impact of digital technologies on business processes and functional areas (blockchain technology, business application patterns, digital twin of an organisation); understanding digital transformation as change management; verification and simulation of IOT data for production processes, semantic search (NLP); building the innovation process and its KPIs, evaluation and prioritisation of ideas, practical application of innovative methods such as business model generation, lean canvas and "blue ocean strategy" to deepen the ideas to the traditional business plan. Digital trends such as Open Innovation, frameworks and examples of successful innovation, Lean StartUp process vs. "traditional" product development. Digitalisation and its impact/risk assessment on companies and leaders in terms of resilience; digital leadership in practice.

Learning Outcomes:

Alumni have an overview of the topics of digital innovation & digital transformation. They have knowledge of how the digital economy works (Industry 4.0, sharing economy, platform economy) and have a basic understanding of the importance of digital transformation for business processes and models. The alumni have mastered process models and methods in order to build up, use and establish a measurable innovation culture in the company and the associated innovation process in the long term. Furthermore, they are able to assess the innovative power of a company with qualitative and quantitative methods and are able to transform ideas into innovations, to use current methods for sharpening and to address common problems in the implementation of ideas in advance and to establish an appropriate innovation process.

Superior module:

IT-Management und Innovation

Module description:

xxx

Master Expose

Semester 3
Academic year 2
Course code ITSM3MAEIT
Type IT
Kind Compulsory
Language of instruction German
SWS 1
ECTS Credits 2
Examination character immanent

Lecture content:

Development of a fully comprehensive exposé as a basis for the supervision process; coherent description of the motivation or problem formulation, the objectives, the research questions, the methodological approach, the expected results of the Master's thesis, the structure of the Master's thesis as well as, in addition, a preliminary bibliography and a viable time schedule.

Learning Outcomes:

The alumni write down all required content-related exposé components and independently coordinate them with the supervisor and ultimately obtain the supervisor's approval. approval by the supervisor. A binding timetable with work steps and milestones is available, whereby the degree of complexity of the topics and questions of the Master's thesis is appropriate to the time and material resources.

Superior module:

Wissenschaftliches Arbeiten

Module description:

xxx

Master Seminar

Semester 3
Academic year 2
Course code ITSM3MASSE
Type SE
Kind Compulsory
Language of instruction German
SWS 1
ECTS Credits 3
Examination character immanent

Lecture content:

Systematic structure of an exposé and discursive defence of initial versions in group situations; characteristics of a scientific working style; literature phase and thematic breadth (variants); theoretical frame of reference; analysis of current publications; dealing with scientific literature sources - also in electronic form - including referencing; quality aspects of scientific work and "standards of good scientific practice".

Learning Outcomes:

The alumni independently carry out goal-oriented structure and content development for scientific work, they independently find relevant publications on the topic area of the Master's thesis and independently build up scientific lines of argumentation, they understand the importance of scientific-methodical procedures. They know the publication life cycle including the review process. Furthermore, they can evaluate formal, structural and content-related quality aspects of scientific work.

Superior module:

Wissenschaftliches Arbeiten

Module description:

xxx

R&D Project 2

Semester 3
Academic year 2
Course code ITSM3RDPPT
Type PT
Kind Compulsory
Language of instruction German
SWS 4
ECTS Credits 4
Examination character immanent

Lecture content:

Research and development-oriented project work with technical-methodical processing of topics of the specialisations. Focus on analysis, evaluation and selection of applied methods and technologies. Practical implementation of technical projects, partly in cooperation or coordination with commercial enterprises. Accompanying project management, reflection and coaching on teamwork as well as preparation and target group-oriented communication of the project results.

Learning Outcomes:

The alumni are able to plan, process and present a research and development-oriented project work in a team. They find independent solutions in the context of scientific evidence (research competence as well as corresponding evidence) and acquire practice-oriented problem-solving competence. This sensitises them to those areas that require in-depth, self-directed knowledge acquisition carried out in a team.

Superior module:

Projekt und Workshop 2

Module description:

xxx

Selected Topics Industrial Informatics 2

Semester 3
Academic year 2
Course code ITSM3AKIIL
Type IL
Kind Compulsory
Language of instruction German
SWS 1
ECTS Credits 2
Examination character immanent

Lecture content:

Acquisition of a basic understanding of a selected topic / a basic technology from the field of "Industrial Informatics"; reflection and critical discussion on the topic in connection with social issues (economic, ecological, social, etc.). Possible topics: OPC Unified Architecture, Mobile & Pervasive (Business) Applications, Advanced Service Engineering, Artificial Intelligence, Systems Engineering, etc. Announcement of the topic focus at the end of the 1st semester. Instead of this course, two alternatives are possible, which are credited in the course of an expression of interest and self-organised learning (within the framework of 4 ECTS/ 2 SWS): Alternative 1: Short Term Mobility: Exchange programme at a partner university, time window between 2nd and 3rd semester. Alternative 2: Organisation Laboratory: Cooperation with the University of Klagenfurt, block course 2nd semester

Learning Outcomes:

The alumni get to know a new basic technology ("State of the Art Method") from the field of "Industrial Informatics", which has not been taught much in the study programme so far. They are able to describe and differentiate between complex technical contents and assign them to possible areas of application. At the same time, they can reflect and critically question their effects on social issues.

Superior module:

Projekt und Workshop 2

Module description:

xxx

SP: Cyber Security 2

SP: Networks for Industry and Critical Infrastructures

Semester 3
Academic year 2
Course code ITSM3NICIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Industrial networks and protocols, energy information networks, real-time requirements for production and critical infrastructure, legacy protocol handling.

Learning Outcomes:

Alumni know the requirements for special networks in the areas of industry and critical infrastructure, especially energy supply. They know current protocols and techniques for these areas and how to handle legacy protocols.

Superior module:

SP: Cyber Security 2

Module description:

xxx

SP: OT Security

Semester 3
Academic year 2
Course code ITSM3OTSIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Industry 4.0 requires ever greater networking of cyber physical systems (CPS) and smart machines and products shift the need for security from a central unit towards a massively distributed system. Classic IT security often falls short here and can only provide security to a limited extent in highly volatile (production) or difficult-to-control (IOT) environments. Methods that can hinder smooth production or normal operation can also not be used economically. On the one hand, the course opens up new perspectives for students with a security focus and, on the other hand, develops strategies, methods and architectures for a secure, undisturbed and usable security standard in production and for networked products. The most important industrial protocols and architecture patterns and their contribution to security at OT level are taught and strategies for usability and security in IOT devices are developed. Current issues from research and industry partners are presented in the course. Participants discuss, develop and present possible solutions and document the state of the art as well as the advantages, disadvantages and technical details of the proposed solutions.

Learning Outcomes:

Alumni are able to distinguish between IT and OT security. They know the most important protocols and architectures of Industrie 4.0 and (I)IOT and they can independently develop strategies to create a comprehensive security concept for product and production.

Superior module:

SP: Cyber Security 2

Module description:

xxx

SP: Data Science & Analytics 2

SP: Deep Learning

Semester 3
Academic year 2
Course code ITSM3DLGIL
Type IL
Kind Elective
Language of instruction English
SWS 3
ECTS Credits 5
Examination character immanent

Lecture 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.

Learning Outcomes:

xxxThe alumni know both basic and current approaches and methods from the areas of deep learning and representation learning and are able to apply these to data sets with suitable toolboxes. In practical tasks, they examine the model construction and the choice of model parameters and decide on the use of pre-trained models in terms of transfer learning. They know methods of semi-supervised learning and data enrichment to optimise effectiveness on small data sets with domain knowledge (Small Data Challenge). They parameterise the respective learning algorithms and apply them to data sets with optimal use of hardware and software resources. They are able to develop innovative applications with these methods and know the limits and areas of application of the respective algorithms.

Superior module:

SP: Data Science & Analytics 2

Module description:

xxx

SP: Natural Language Processing

Semester 3
Academic year 2
Course code ITSM3NLPIL
Type IL
Kind Elective
Language of instruction English
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Natural Language Processing with Deep Neural Networks, for example recurrent neural networks, attention models, transformers or BERT. Contextualised representations, subword tokenisation, beam search. Applications: transfer learning, text classification, text generation, machine translation. Tools: Python, scikit-learn, nltk, tensorflow/keras/PyTorch.

Learning Outcomes:

Alumni will be able to apply so-called attention-based natural language processing models and implement suitable networks for applications in areas such as machine translation and sentiment analysis in social networks. Building on previously acquired skills in pre-processing text data, they are able to use contextualised text representations and complex network architectures for this purpose. They are able to determine network parameters and design depending on the problem and know the limits and application areas of the respective algorithms.

Superior module:

SP: Data Science & Analytics 2

Module description:

xxx

SP: Smart Systems & Robotics 2

SP: Mobile Robotics

Semester 3
Academic year 2
Course code ITSM3MRIKIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Components of a mobile robot, types of motion of mobile robots, sensors and actuators on the mobile robot, introduction to localisation, mapping in 2D/3D, motion planning, robot control architectures, introduction to ROS.

Learning Outcomes:

The alumni know the most important components of a mobile robot and can compare the different types of motion of a mobile platform. They know the concepts of motion planning for mobile robots and can apply them. They understand strategies of perception for robots. They know methods of localisation, mapping, and navigation and their strengths and weaknesses and knowledge of robot control architectures. They can relate the methods they have learned to a given mobile robot platform and program it with ROS in a hardware-oriented way.

Superior module:

SP: Smart Systems & Robotics 2

Module description:

xxx

SP: Numerical and Industrial Algorithms

Semester 3
Academic year 2
Course code ITSM3NIAIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

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

Learning Outcomes:

The alumni know the numerical difficulties in programming, can quantify them and show possible solutions. They know different numerical and geometric methods, can implement these in simple cases, can apply library implementations and make comparative selections.

Superior module:

SP: Smart Systems & Robotics 2

Module description:

xxx

WF1: Informationstechnologien

WF1: Big Data and Cloud Computing

Semester 3
Academic year 2
Course code ITSM3BDCIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Paradigms and characteristics of Big Data and cloud computing; overview of common Big Data frameworks and business-relevant cloud infrastructures; programming techniques for data-intensive applications and use of hybrid cloud-based infrastructures for data-intensive software development; implementation of case studies; selected chapters from Big Data Computing.

Learning Outcomes:

Alumni master the technical and organisational challenges of Big Data processing and apply methods and techniques of data-intensive software development. They apply common Big Data frameworks and use the transdisciplinary aspects of cloud computing and communicate its technological foundations. Furthermore, they implement selected case studies of data-intensive business applications.

Superior module:

WF1: Informationstechnologien

Module description:

xxx

WF1: Dependable Systems Engineering

Semester 3
Academic year 2
Course code ITSM3DSEIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Challenges and approaches for the interdisciplinary development of software-intensive systems; basics of systems engineering; Model Based Systems Engineering (MBSE); dependability as a collective term for critical qualities, systems engineering process models; verification, validation and integration in an interdisciplinary environment; linking technical architectures with organisational architectures.

Learning Outcomes:

Alumni have an awareness of specific challenges in the interdisciplinary development of software-intensive systems. They know different concepts and methods (such as Model Based Systems Engineering, MBSE) to meet these challenges. Furthermore, they have a deeper understanding of the use and application of SysML (Systems Modelling Language) and ISO 15288 as a fundamental process model. In addition, they know the importance of critical qualities (dependability, reliability) in the context of interdisciplinary development.

Superior module:

WF1: Informationstechnologien

Module description:

xxx

WF1: Industrial Image Processing

Semester 3
Academic year 2
Course code ITSM3IIPIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Basic elements of the image processing chain, hardware components (optics, cameras, illumination), basics of image processing (filtering, image enhancement, image segmentation), morphological operations, image analysis in the time & frequency domain, industrial inspection, visual quality control, basics of image learning

Learning Outcomes:

The alumni know the essential hardware components of an industrial image processing system and are aware of their characteristics and possible applications. They master the theory of the most important methods and algorithms and can implement them using common software libraries. You are able to analyze to evaluate image processing tasks in order to develop solutions for industrial image processing. image processing. You will know simple concepts of machine learning and their their applicability in image processing.

Superior module:

WF1: Informationstechnologien

Module description:

xxx

WF2: Informatik bzw. Management

WF2: Business Leadership and Startup

Semester 3
Academic year 2
Course code ITSM3BLSIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

The aim of this course is to understand how classical corporate management and digital leadership work and their impact on companies and managers; special focus on modern management approaches such as digital leadership in practice, agile leadership; leading virtual teams; digitalization & agility; dealing with distance; trust as a success factor; dealing with virtual communication and with conflicts in the virtual space; eLearning tools in the company. Introduction and consolidation in corporate leadership or management, leadership theories, leadership styles, management function/tasks, management process/systems target systems, planning, decision-making, organization, leadership, controlling, management process - application with examples of a corporate development; leadership models (transactional, transformational), application of key performance indicator systems; realization and success opportunities, change management, corporate development, lean management, human resources management, time management, coordination and conflict management. Company formation (company formations, business formations) Start-up management, developing a business idea, business case & product innovation (using business canvas & value proposition canvas); types of financing (focus: capital market); virtual companies, developing a business plan, conducting innovative business games ("Apollo 13" or "Target SIM"). Theoretical contents are deepened with technology-focused case studies from the business world.

Learning Outcomes:

The alumni have an overview of the topics of business management and business start-up. They are familiar with the structure, interrelationships and processes within a company. They know the management cycle and are able to use the most essential instruments of business management. They will be able to draw up a business plan, and also differentiate between the various models of - increasingly digital - leadership and related procedures, strengths and weaknesses and differences, and assess the effects on corporate culture.

Superior module:

WF2: Informatik bzw. Management

Module description:

xxx

WF2: Energy Informatics

Semester 3
Academic year 2
Course code ITSM3ENIIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Fundamentals Energy Informatics, smart grids, smart metering, energy markets, smart energy systems.

Learning Outcomes:

Alumni are familiar with the challenges of modern energy systems and know the basics of energy informatics. They know the most important protocols in digitalized energy systems and solutions on different grid levels.

Superior module:

WF2: Informatik bzw. Management

Module description:

xxx

WF2: UX Technologies

Semester 3
Academic year 2
Course code ITSM3UXTIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Basic principles of user experience design and interaction and dialog design; design-thinking process; use of graphical development environments; programming techniques for simple multimodal user interactions and heterogeneous platforms; graphical representation of mathematical-technical facts; use of basic GUI elements such as menus, windows, dialogs and their modalities; basic concepts of speech- and gesture-based interaction; development tools for UI creation; interaction with mobile systems; selected chapters of current UX technologies.

Learning Outcomes:

Alumni demonstrate in-depth understanding of the UX design process and apply the basic principles of user experience design and especially human-computer interaction using current technical tools and interaction techniques to implement their own solutions. In doing so, they consistently implement a UX-oriented information architecture using industry-relevant tools and development environments to design simple to sophisticated interaction-optimized applications for multiple target platforms (e.g., Android, iOS, or Windows) and implement them. You will independently develop your own UX portfolio based on common UX technologies.

Superior module:

WF2: Informatik bzw. Management

Module description:

xxx

Course titleSWSECTSTYPE

Lecture Series

Semester 4
Academic year 2
Course code ITSM4RVORC
Type RC
Kind Compulsory
Language of instruction German
SWS 1
ECTS Credits 1
Examination character immanent

Lecture content:

Panel or short presentations followed by discussion from various R&D projects of the program and from collaborations with companies on current topics. Literature reviews.

Learning Outcomes:

Alumni learn about current application scenarios in the area of the core subjects of the curriculum, reflect together with stakeholders and actors on the impact of the use of digital technologies and are able to transform these insights into experiential knowledge for their future work.

Superior module:

Sozialkompetenz und Kommunikation 2

Module description:

xxx

Master Exam

Semester 4
Academic year 2
Course code ITSM4MAPDP
Type DP
Kind Compulsory
Language of instruction German
SWS 0
ECTS Credits 2
Examination character final

Lecture content:

The final Master's examination consists of the examination parts (1) presentation of the Master's thesis in English including defensio of the Master's thesis, (2) examination discussion, which deals with the cross-connections of the topic of the Master's thesis to the relevant subjects of the knowledge lines of the curriculum, as well as (3) an examination discussion about other curriculum-relevant contents in connection with innovation references of the approved Master's thesis.

Learning Outcomes:

The alumni present coherently and concisely the motives, the methods used and the results achieved in their master's theses and give a well-informed outlook on the future; they answer the questions asked about their master's thesis in a manner appropriate to the target audience. In addition, they establish easily comprehensible cross-connections to essential reference subjects of the course of study and communicate the innovation aspects of their Master's theses in a generally comprehensible manner, explaining complex interrelationships in a way that is relevant to the target audience. Through the Master's degree examination, alumni demonstrate their ability to "act in uncertainty" and prove their ability to respond adequately to challenging questions through confident demeanor and solid technical-scientific argumentation.

Superior module:

Wissenschaftliches Arbeiten

Module description:

xxx

Master Thesis

Semester 4
Academic year 2
Course code ITSM4MAAIT
Type IT
Kind Diploma/master thesis
Language of instruction German
SWS 0
ECTS Credits 20
Examination character immanent

Lecture content:

Development and independent processing of questions as well as content-related discussion at a scientific level on a current topic of information technologies (core subject areas represent the lines of knowledge of the research at the study program) with special consideration of the innovation potential of the targeted solutions as well as adherence to a scientifically oriented approach argued at the current state of the literature.

Learning Outcomes:

The alumni independently compose their written master thesis and proceed scientifically and systematically. They analyze and present problems and identify corresponding research questions and objectives, formulate hypotheses and independently implement the necessary work steps. They develop the content of the master's thesis oriented towards the lines of knowledge of the research at the study program, whereby the alumni scientifically argue and justify their approach and critically question their results.

Superior module:

Wissenschaftliches Arbeiten

Module description:

xxx

Targeted Communication

Semester 4
Academic year 2
Course code ITSM4ZGKIL
Type IL
Kind Compulsory
Language of instruction German
SWS 2
ECTS Credits 2
Examination character immanent

Lecture content:

Identifying contact persons and methods to reach them. Forms and frameworks of effective feedback, exercises and role plays. Individual processing of the inputs presented and developed in the integrated course, supported by selective coaching.

Learning Outcomes:

Alumni are able to present complex content in a target group-oriented manner, developing clearly structured lines of argumentation. They are able to argue in a solution- and benefit-oriented manner, as well as to formulate criticism in a factual and constructive manner. They are able to accept criticism and report it back accordingly.

Superior module:

Sozialkompetenz und Kommunikation 2

Module description:

xxx

SP: Cyber Security 2

SP: Advanced Topics Networking, Security and Privacy

Semester 4
Academic year 2
Course code ITSM4ATNIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Current topics in network technologies, security and privacy.

Learning Outcomes:

Alumni are familiar with the latest technologies and the state of research in selected areas. They are able to acquire scientific literature in these areas and to carry out simulations and implementations on the basis of this literature.

Superior module:

SP: Cyber Security 2

Module description:

xxx

SP: Data Science & Analytics 2

SP: Current Trends in AI

Semester 4
Academic year 2
Course code ITSM4CTAIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

The contents are based on current research topics and collaborations of the Applied Data Science Lab of the Salzburg University of Applied Sciences and are offered in the form of guest lectures, special labs, article reviews and workshops. The respective offering will be developed and announced in the winter semester. The course can also be taken in the form of ECTS credits from other technical Master's programs related to the topic.

Learning Outcomes:

Together with researchers and experts, alumni develop and discuss new applications and technologies in the field of artificial intelligence. They are able to study scientific articles and deal with challenges and approaches in companies. They are able to reflect on the impact of technology and its social and ethical implications.

Superior module:

SP: Data Science & Analytics 2

Module description:

xxx

SP: Smart Systems & Robotics 2

SP: Modern Industrial Automation

Semester 4
Academic year 2
Course code ITSM4MIAIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Overview of conceptualization and change to modern industrial automation, architecture of and communication in distributed industrial systems (RAMI 4.0, OPC UA, Pub-Sub, real-time communication), security in industrial systems, information modeling (OPC UA, Companion Specifications), industrial integration of AI methods (e.g. Machine Learning, Analytics, Computer Vision).

Learning Outcomes:

Alumni will be able to evaluate and critically assess the challenges of modern industrial automation. They can derive, design and elaborate solutions suitable for the requirements of secure, flexible and distributed automation. Alumni know the current architecture and information models and can classify and relate them and justify a selection. They can demonstrate and accompany the transition to modern industrial automation for selected use cases. They know the specific challenges of industrial AI methods and can take these into account in their own designs.

Superior module:

SP: Smart Systems & Robotics 2

Module description:

xxx

Legend
SemesterSemesters 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)
SWSweekly contact hours over 14 weeks in semester (example SWS 2 equals 28 contact hours for the whole course
ECTS CreditsWork load in ECTS credits, 1 ECTS credit equals an estimated 25 hours of work for the student
TypeBP = 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