Information Technology and Systems Management

Course titleSWSECTSTYPE

Advanced Presentation Skills

Semester 1
Academic year 1
Course code ITBM1APSIL
Type IL
Kind Compulsory
Language of instruction English
SWS 1
ECTS Credits 2
Examination character immanent

Lecture content:

Summary of applicable professional texts and scientific articles and their preparation for verbal presentation, target group oriented and emotionally appealing presentation techniques, use of rhetorical techniques, story-boarding and storytelling.

Superior module:

Communication Skills

Module description:

Using relevant specialist literature, graduates are able to independently work on, orally present and defend a given topic. They can apply the acquired rhetoric skills in discussions and presentations. Graduates have a basic understanding for the complex factors essential to intercultural communication such as stereotypes, different traditions and implications of gestures, intonation or salutations. They are able to recognise and discuss their own culture-based role in the communication context and to consequently deduct the respective actions.

IT Management

Semester 1
Academic year 1
Course code ITBM1ITMIL
Type IL
Kind Compulsory
Language of instruction German
SWS 3
ECTS Credits 3
Examination character immanent

Lecture content:

IT portfolio as strategic controlling instrument; product and services calculation service level agreements, service level management, strategic IT controlling; IT billing; resource planning and capacity management; TCO analysis; procurement management; IT departments and organisation structures; IT in/out-sourcing; knowledge management, ERP systems (e.g. SAP), IT governance (Sarbanes-Oxley Act, BASELII, legal provisions), ITIL (optional: ITIL foundation - certification), COBIT, IT finance management, ISO17799 and ISO20000, change management, problem management (help desk), integrate necessary IT tools, integrate technical 'know-how' in a whole-business and organisational environment, the role of the IT management in the company, IT management standards, regulations and laws, identify elements of an IT strategy, plan and prepare IT business model for innovative services, service support and service delivery, security management, life-cycle management, disaster recovery measures; back-up/restore plans (emergency planning).

Superior module:

IT Mangement and IT Security

Module description:

Graduates have the necessary knowledge to successfully design and manage the IT infrastructure in a company (strategic IT controlling). They can analyse the relevant operational, legal and social environment. Graduates have the ability to design the IT infrastructure in line with the organisation of the enterprise and understand the IT as part of the core business process. They can plan and monitor IT projects (product and service calculation) and the customer-oriented design of IT services and security management. They have specific knowledge in IT controlling, ERP systems, process management and e-commerce and are able to design and to protect up-to-date IT systems as well as analyse compromised systems. Furthermore, they can discuss national as well as international standards relevant to IT security and are able to implement these.

IT Project Management

Semester 1
Academic year 1
Course code ITBM1IPMIL
Type IL
Kind Compulsory
Language of instruction German
SWS 2
ECTS Credits 2
Examination character immanent

Lecture content:

Planning product innovation: Project definition, task structure, quality assurance, work packages, organisation, roles, phases, milestones/results, flowcharts, multi-project steering; implementation and controlling: Conflict line/project, progress checking, prognostics, risk analysis, reporting, qualitative and quantitative assessment, documentation, software models and tools; social competence: Teamwork, challenge, expectations, self-organisation, moderation, feedback, management styles, roles in the team, process-oriented and agile procedure models (Scrum), multi-project management, management of outsourcing projects, IT project manager as change agent, systematic planning and structuring of projects, profitability analysis, project marketing, analysis and influence of behavioural patterns in the project organisations. Coaching of projects, certification for the process manager by "Project Management Austria" is optional.

Superior module:

IT Project- and Qualitymanagement

Module description:

Graduates are aware of the principles, methods and processes of project management, also in virtual systems (POOL - ProjectOrganisationOnLine) and the prerequisites for successful innovations of any kind. Graduates can describe the design and content of a quality management system. They are able to interpret the contents of the relevant QM norms, implement them for the respective organisation and document them in a quality management manual. They can distinguish between the most common methods for quality control (SPC), acceptance sampling, evaluation procedures as well as quality improvement using testing methodology and quality assurance in virtual systems (monitoring); incident management, service level management and reporting.

R&D Project 1

Semester 1
Academic year 1
Course code ITBM1PROPT
Type PT
Kind Compulsory
Language of instruction German
SWS 4
ECTS Credits 5
Examination character immanent

Lecture content:

Research and development oriented project work with technical methods for processing topics of the specialisations. Practical implementation of technical projects, in part in cooperation with or in agreement with commercial enterprises. The focus here lies on the one hand with the analysis, assessment and selection of the methods applied or the technologies used, and on the other hand on the recognition and creation of interdisciplinary interdependencies.

Superior module:

R&D Project

Module description:

Graduates are able to carry out research and development-oriented project work in the context of the chosen specialisation and are able to independently devise solutions based on scientific evidence and document these accordingly. They can apply practice-oriented problem-solving skills; they can identify areas requiring self-directed acquisition of in-depth knowledge.

Selected Topics in Information Technologies 1

Semester 1
Academic year 1
Course code ITBM1INTIT
Type IT
Kind Compulsory
Language of instruction German
SWS 3
ECTS Credits 3
Examination character immanent

Lecture content:

The selection of detailed content is aligned with the prior knowledge and selected specialisations in the masters' course.

Superior module:

Selected Topics in Information Technologies

Module description:

The students know the necessary basics of information technology required for their selected specialisations. For the individual specialisations this includes: Adaptive software systems: The students know current techniques and methods in the area of media informatics and can design simple multimedia systems and drive the technical implementation. Signal processing & robotics: The students know the theoretical basis of signal and system theory and understand the main practical interrelations. Data science and analytics: Students understand the theoretical basis of probability calculation and can apply methods of descriptive statistics to concrete data records by means of matlab. Computer networks and IT security: Students know the theoretical basics of routing and switching and are able to design and construct simple network infrastructure. Energy informatics: Students know the theoretical basics of electrical systems and have an overview of the basics of network technology.

Selected Topics in Mathematics and Modelling

Semester 1
Academic year 1
Course code ITBM1AKMIL
Type IL
Kind Compulsory
Language of instruction English
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Selected chapters from analysis, algebra, numerics are presented theoretically and implemented by the students with the help of Matlab and using concrete tasks: Multidimensional differentiation and integration, vector spaces with inner product, length and angle measurement, standards, metrics, eigenvalue theory and matrix decomposition; convex optimisation: processes and concepts (gradient descent, linear/quadratic programming and duality, least squares and regularisation)

Superior module:

Mathematics and Modelling

Module description:

The students are able to understand mathematical formulations in specialist articles, particularly in the field of IT, apply methods of mathematical modelling to concrete problems and to implement these in corresponding algorithms. They are able to use methods of estimation theory and inferential statistics to analyse complex tasks, in particular in the field of machine learning. They understand the mathematical basic of convex optimisation and matrix decomposition and can resolve statistical tasks with the help of mathematics software and visualise the data accordingly.

Software Notations

Semester 1
Academic year 1
Course code ITBM1SWNIL
Type IL
Kind Compulsory
Language of instruction German
SWS 2
ECTS Credits 2
Examination character immanent

Lecture content:

Textual and graphical notation (e.g. UML as standard notation) for software development and modelling; requirements engineering; notation for interface specifications; lexer, parser incl. scanner; use of current notation tools; use of domain-specific UML profiles; validation and verification; meta-modelling; current topics of software notation.

Superior module:

Software Technologies

Module description:

Graduates are able to carry out formalised descriptions of various artefacts of software development. They are proficient in the common types of UML diagrams and are able to employ them to develop a system. They are able to use and evaluate CASE tools and to apply methods and tools of the platform-independent software development. They are proficient in abstraction concepts of model-driven software development. Furthermore, graduates understand the different scopes of duty and operations in the context of the software development process and can apply advanced knowledge in the organisation of software projects. Graduates are able to evaluate various procedure models and independently promote the conception and development of professional software projects.

SPEC: Adaptive Software Systems 1

SPEC: Advanced Software Technologies

Semester 1
Academic year 1
Course code ITBM1SWTIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Application-specific communication paradigms; communication techniques for time-dependent data streams; distributed systems and distributed data management; overview of component technologies; migration concepts for MM data and software; use of application servers; current topics and application examples of the software technologies.

Superior module:

SPEC: Adaptive Software Systems 1

Module description:

Graduates have extensive knowledge as regards distributed software systems, distributed data storage and distributed data management. They know about current component technologies and middleware systems and are proficient in methods and tools of platform-independent software development. Furthermore, graduates understand modern software architecture and the significance of architecture decisions for development projects. They are proficient in special aspects and profiles of current software notations (UML) as a notation for software architectures. They have a good command of software design patterns as well as architecture patterns and their application and can comprehend informatical abstraction methods and apply. They are capable of analysing standard publications (e.g. LNCS) and independently identifying problems relevant to research and innovation as well as developing solutions.

SPEC: Computer Networks and IT-Security 1

SPEC: Internet Infrastructure and Security

Semester 1
Academic year 1
Course code ITBM1IISIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Attack types, threat vectors, risk assessment, firewalls, VPNs, security protocols, IPSec, TLS/SSL, advanced routing protocols and concepts, routing protocol authentication, BGP, load balancing.

Superior module:

SPEC: Computer Networks and IT-Security 1

Module description:

Graduates acquire knowledge and practical skills in the operation and design of mobile as well as fixed networks. They know how to create, evaluate and optimise architectures and processes in these networks. Graduates acquire the abilities necessary for the configuration of network components and their performance evaluation. They can identify potential threats to network infrastructures and know counter-measures.

SPEC: Data Science and Analytics 1

SPEC: Feature Generation

Semester 1
Academic year 1
Course code ITBM1FEGIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

The goal of this course is to be able to generate features from different data sources, which can be used as inputs for machine learning and knowledge discovery algorithms. A primary focus here is image data and natural language texts. Introduction to data science and analytics; data, pre-processing, outlier detection, cleaning and incomplete data; complexity considerations; relevant terms from information theory; parametrised and non-parametrised models for data; annotated data; explorative data analysis image processing: Acquisition of detailed knowledge of the following concepts/algorithms: Low-level features (edges, localized & geometric features), high-level features I (shapes, objects, Hough transforms), interest point/feature detectors (SIFT, SURF, HOG), image classification (visual bag of words), high-level features II (object & region descriptors), texture description (normalized Fourier coefficients, co-occurrence matrix, local binary patterns). Practical application of the algorithms through partially independent implementation and also through the use of libraries (Matlab/Python/OpenCV) natural language processing: Basics of information retrieval (corpus, index, tokenisation, stopping, stemming, ngrams), retrieval models, search engines, crawling, link analysis, ranking, language models (vector representations (word2vec), BoW); Python programming language; familiarity with different libraries for text processing and machine learning (e.g. NLTK, scikit-learn, pandas)

Superior module:

SPEC: Data Science and Analytics 1

Module description:

The data science and analytics 1 module introduces the students to the classical setup for the extraction of knowledge from data. Students are able to collect, prepare, further process and visualise data and in doing so to pursue the goal of applying procedures for machine learning. They create complex applications to process raw data such as images, natural language texts and signals, whereby data decisions are made, situations classified, objects detected and prognoses created. In doing so, they use state-of-the-art toolboxes and scalable technologies and can provide substantiated arguments for the selected procedures.

SPEC: Digital Signal Processing

SPEC: Digital Signal Processing 1

Semester 1
Academic year 1
Course code ITBM1DSVIL
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 Fourier transforms (DFT) and fast Fourier transforms (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 and implementation with software (laboratory), principle and design of IIR filters.

Superior module:

SPEC: Digital Signal Processing

Module description:

Graduates know and understand basic algorithms for digital signal processing like FFT, convolution and correlation of discrete signals and are familiar with their application. They can describe discrete systems mathematically with the help of z-transforms and can determine system responses and frequency responses and transform systems from s-level to z-level. They know the principles of digital filters and are able to develop FIR and IIR filters with the aid of mathematical tools; furthermore, they are familiar with the principle of adaptive filters. Graduates know basic algorithms for image processing and pattern recognition. They understand simple, digital controllers and can design these with the help of mathematical tools and implement them in software.

SPEC: Energy Informatics 1

SPEC: Fundamentals in Energy Informatics

Semester 1
Academic year 1
Course code ITBM1EGLIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Option 1 > energy systems: Electrics, distribution networks (network levels, network structures, network requirements), energy generation, exercises and laboratory: Dimensioning electrical systems, simulation of a solar installation. // Smart Grid architecture and components: Virtual power stations, storage, monitoring and control, intelligent distribution networks, demand-response management. Option 2 > communication technologies: GSM&GPRS/UMTS/LTE: Protocols, architectures, layer 1 (CDMA), basics for wide area networks, crypto-methods in LAN/WLAN, network management (SNMP, QoS DiffServ, SDN), MPLS, M2M (machine to machine). // Smart Grid architecture and components: Virtual power stations, storage, monitoring and control, intelligent distribution networks, demand-response management.

Superior module:

SPEC: Energy Informatics 1

Module description:

Graduates can describe the structure and functionality of energy systems as well as the specific ICT needs related to their architecture. They can explain the relevant architectures, interfaces and protocols (SCADA, IEC standards, new developments). They are able to identify security issues, assess potential threats and apply appropriate measures in dealing with them. They can configure and test future energy systems components (e.g. smart meter, aggregator, substation).

Course titleSWSECTSTYPE

Applied Statistics

Semester 2
Academic year 1
Course code ITBM2ASTIL
Type IL
Kind Compulsory
Language of instruction English
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

The objective of this course, alongside the acquisition of essential statistical methods (such as statistical tests or parameter estimation) is being a support to the pattern recognition course to be held in the following semester. The estimators and tests will therefore be introduced and applied primarily for the assessment and comparison of various models of supervised learning. Density estimation is a statistical method used (i) for visualisations in data mining and (ii) a basic building block of machine learning models, just as the Naive Bayes classifier. (Binary) Classification and design cycle, confusion matrix and performance metrics; point and interval estimator (KI), for example, Bernoulli estimators; Overview: Estimator for average values, expectations and most important parameters; statistical tests: formation of hypotheses, significance level, faulty types (sensitivity, specificity, precision, recall); presentation of data and kernel density estimator

Superior module:

Mathematics and Modelling

Module description:

The students are able to understand mathematical formulations in specialist articles, particularly in the field of IT, apply methods of mathematical modelling to concrete problems and to implement these in corresponding algorithms. They are able to use methods of estimation theory and inferential statistics to analyse complex tasks, in particular in the field of machine learning. They understand the mathematical basic of convex optimisation and matrix decomposition and can resolve statistical tasks with the help of mathematics software and visualise the data accordingly.

Discussion and Argumentation Skills

Semester 2
Academic year 1
Course code ITBM2DASIL
Type IL
Kind Compulsory
Language of instruction English
SWS 1
ECTS Credits 2
Examination character immanent

Lecture content:

Argumentation, negotiations and discussion techniques, use of corresponding phrases and rhetorical techniques, practical examples and role-playing.

Superior module:

Communication Skills

Module description:

Using relevant specialist literature, graduates are able to independently work on, orally present and defend a given topic. They can apply the acquired rhetoric skills in discussions and presentations. Graduates have a basic understanding for the complex factors essential to intercultural communication such as stereotypes, different traditions and implications of gestures, intonation or salutations. They are able to recognise and discuss their own culture-based role in the communication context and to consequently deduct the respective actions.

IT Quality Management

Semester 2
Academic year 1
Course code ITBM2IQMIL
Type IL
Kind Compulsory
Language of instruction German
SWS 2
ECTS Credits 2
Examination character immanent

Lecture content:

Detailed explanation of the EN ISO 9000ff set of standards and other standards relevant for the IT-relevant standards; implementation of quality management systems (best practices), information security management systems per ISO 27001 - requirements for an information security management system, ISO 27002 - guidelines for the introduction of an information security management system (practical examples), application of appropriate methods for fulfilling the individual normative requirements, application of methods for continuous improvement of business efficiency with regard to the quality of the organisation, quality monitoring in distributed systems. Furthermore, the legal basis for the impairment of performance area of civil law is also conveyed to the students. The presentation of quality management as a cross-disciplinary function, as covered in the ITS Masters course, is a major objective.

Superior module:

IT Project- and Qualitymanagement

Module description:

Graduates are aware of the principles, methods and processes of project management, also in virtual systems (POOL - ProjectOrganisationOnLine) and the prerequisites for successful innovations of any kind. Graduates can describe the design and content of a quality management system. They are able to interpret the contents of the relevant QM norms, implement them for the respective organisation and document them in a quality management manual. They can distinguish between the most common methods for quality control (SPC), acceptance sampling, evaluation procedures as well as quality improvement using testing methodology and quality assurance in virtual systems (monitoring); incident management, service level management and reporting.

R&D Project 2

Semester 2
Academic year 1
Course code ITBM2PROPT
Type PT
Kind Compulsory
Language of instruction German
SWS 4
ECTS Credits 5
Examination character immanent

Lecture content:

Continuation of the research and development oriented project work with technical methods for processing topics of the specialisations. The focus is on the practical implementation of technical projects, in part in cooperation with or in agreement with commercial enterprises as well as the target group oriented preparation and communication of the project results.

Superior module:

R&D Project

Module description:

Graduates are able to carry out research and development-oriented project work in the context of the chosen specialisation and are able to independently devise solutions based on scientific evidence and document these accordingly. They can apply practice-oriented problem-solving skills; they can identify areas requiring self-directed acquisition of in-depth knowledge.

Security Management

Semester 2
Academic year 1
Course code ITBM2SECIL
Type IL
Kind Compulsory
Language of instruction German
SWS 3
ECTS Credits 3
Examination character immanent

Lecture content:

Basic principles, information-risk management, information and data classification, national and international information security standards and frameworks (e.g. ISO27000, IT basic protection), 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 and malware forensics, incident handling and computer emergency response team (CERT), special legal bases and legal characteristics from the telecommunications laws and data protection laws, patent law and trademark law.

Superior module:

IT Mangement and IT Security

Module description:

Graduates have the necessary knowledge to successfully design and manage the IT infrastructure in a company (strategic IT controlling). They can analyse the relevant operational, legal and social environment. Graduates have the ability to design the IT infrastructure in line with the organisation of the enterprise and understand the IT as part of the core business process. They can plan and monitor IT projects (product and service calculation) and the customer-oriented design of IT services and security management. They have specific knowledge in IT controlling, ERP systems, process management and e-commerce and are able to design and to protect up-to-date IT systems as well as analyse compromised systems. Furthermore, they can discuss national as well as international standards relevant to IT security and are able to implement these.

Selected Topics in Information Technologies 2

Semester 2
Academic year 1
Course code ITBM2INTIT
Type IT
Kind Compulsory
Language of instruction German
SWS 3
ECTS Credits 3
Examination character immanent

Lecture content:

The selection of detailed content is aligned with the prior knowledge and selected specialisations in the masters' course.

Superior module:

Selected Topics in Information Technologies

Module description:

The students know the necessary basics of information technology required for their selected specialisations. For the individual specialisations this includes: Adaptive software systems: The students know current techniques and methods in the area of media informatics and can design simple multimedia systems and drive the technical implementation. Signal processing & robotics: The students know the theoretical basis of signal and system theory and understand the main practical interrelations. Data science and analytics: Students understand the theoretical basis of probability calculation and can apply methods of descriptive statistics to concrete data records by means of matlab. Computer networks and IT security: Students know the theoretical basics of routing and switching and are able to design and construct simple network infrastructure. Energy informatics: Students know the theoretical basics of electrical systems and have an overview of the basics of network technology.

Software Engineering

Semester 2
Academic year 1
Course code ITBM2SWEIL
Type IL
Kind Compulsory
Language of instruction German
SWS 2
ECTS Credits 2
Examination character immanent

Lecture content:

Process model and process analysis; software project management; software quality management; expenditure estimation processes; selection and use of product and process metrics; application security and incident management; focus on software engineering techniques for software development“; software engineering tool chain; current topics of software engineering.

Superior module:

Software Technologies

Module description:

Graduates are able to carry out formalised descriptions of various artefacts of software development. They are proficient in the common types of UML diagrams and are able to employ them to develop a system. They are able to use and evaluate CASE tools and to apply methods and tools of the platform-independent software development. They are proficient in abstraction concepts of model-driven software development. Furthermore, graduates understand the different scopes of duty and operations in the context of the software development process and can apply advanced knowledge in the organisation of software projects. Graduates are able to evaluate various procedure models and independently promote the conception and development of professional software projects.

SPEC: Adaptive Software Systems 1

SPEC: Software Architectures

Semester 2
Academic year 1
Course code ITBM2SWAIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Basics and characterisation of software architectures; UML as notation for software architectures; special aspects and profiles of UML; architecture-specific quality attributes; software architecture development and re-engineering; model and service oriented architectures; architectures for migration-capable applications; software architecture assessment and architecture metrics; reference architectures; current topics for software architectures.

Superior module:

SPEC: Adaptive Software Systems 1

Module description:

Graduates have extensive knowledge as regards distributed software systems, distributed data storage and distributed data management. They know about current component technologies and middleware systems and are proficient in methods and tools of platform-independent software development. Furthermore, graduates understand modern software architecture and the significance of architecture decisions for development projects. They are proficient in special aspects and profiles of current software notations (UML) as a notation for software architectures. They have a good command of software design patterns as well as architecture patterns and their application and can comprehend informatical abstraction methods and apply. They are capable of analysing standard publications (e.g. LNCS) and independently identifying problems relevant to research and innovation as well as developing solutions.

SPEC: Computer Networks and IT-Security 1

SPEC: Network Reliability and Virtualisation

Semester 2
Academic year 1
Course code ITBM2NZVIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Advanced LAN design and switching, redundant networks, layer 2 security, VLAN security, advanced network monitoring, intrusion detection & prevention, IPv6 security, virtualised network environments, security in virtualised environments, access control, biometrics, capture the flag contest.

Superior module:

SPEC: Computer Networks and IT-Security 1

Module description:

Graduates acquire knowledge and practical skills in the operation and design of mobile as well as fixed networks. They know how to create, evaluate and optimise architectures and processes in these networks. Graduates acquire the abilities necessary for the configuration of network components and their performance evaluation. They can identify potential threats to network infrastructures and know counter-measures.

SPEC: Data Science and Analytics 1

SPEC: Machine Learning

Semester 2
Academic year 1
Course code ITBM2MALIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Based on the knowledge attained in the 1st. semester (feature generation) about concepts and methods for generating features from data, these are now fed to different systems for classification and regression. Course content includes: Introduction to the theory of monitored machine learning, design cycle for example classical process; generative models (incl. estimation aspects); discriminative models; loss functions and regularisation; corresponding training and learning algorithms and parametrisation; implementation of selected processes with Matlab and special toolboxes (NLTK, scikit-learn)

Superior module:

SPEC: Data Science and Analytics 1

Module description:

The data science and analytics 1 module introduces the students to the classical setup for the extraction of knowledge from data. Students are able to collect, prepare, further process and visualise data and in doing so to pursue the goal of applying procedures for machine learning. They create complex applications to process raw data such as images, natural language texts and signals, whereby data decisions are made, situations classified, objects detected and prognoses created. In doing so, they use state-of-the-art toolboxes and scalable technologies and can provide substantiated arguments for the selected procedures.

SPEC: Digital Signal Processing

SPEC: Digital Signal Processing 2

Semester 2
Academic year 1
Course code ITBM2DSVIL
Type IL
Kind Elective
Language of instruction English
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

Principles of adaptive filters, LMS-FIR filters, basics of image processing, colour spaces, colour triangle, 2D filters, special 2D filters (edge filter, Laplace filter, Gauß filter), image enhancement (brightness adaptation, noise reduction), basics of digital controllers, simple controllers (PID), dead-beat controllers, simulation and development of software (laboratory).

Superior module:

SPEC: Digital Signal Processing

Module description:

Graduates know and understand basic algorithms for digital signal processing like FFT, convolution and correlation of discrete signals and are familiar with their application. They can describe discrete systems mathematically with the help of z-transforms and can determine system responses and frequency responses and transform systems from s-level to z-level. They know the principles of digital filters and are able to develop FIR and IIR filters with the aid of mathematical tools; furthermore, they are familiar with the principle of adaptive filters. Graduates know basic algorithms for image processing and pattern recognition. They understand simple, digital controllers and can design these with the help of mathematical tools and implement them in software.

SPEC: Energy Informatics 1

SPEC: Protocols in Energy Informatics

Semester 2
Academic year 1
Course code ITBM2PENIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 5
Examination character immanent

Lecture content:

IEC 61850, integration Internet (IEC 61850-90-5,-12); laboratory exercises: Substation configuration, tool-based power system analysis; Smart Grid Architecture Model (SGAM).

Superior module:

SPEC: Energy Informatics 1

Module description:

Graduates can describe the structure and functionality of energy systems as well as the specific ICT needs related to their architecture. They can explain the relevant architectures, interfaces and protocols (SCADA, IEC standards, new developments). They are able to identify security issues, assess potential threats and apply appropriate measures in dealing with them. They can configure and test future energy systems components (e.g. smart meter, aggregator, substation).

Course titleSWSECTSTYPE

Data Mining

Semester 3
Academic year 2
Course code ITBM3DMGIL
Type IL
Kind Compulsory
Language of instruction English
SWS 2
ECTS Credits 2.5
Examination character immanent

Lecture content:

The data mining course focusses on unsupervised methods, used mostly for the visualisation of data and to discover new, previously unknown data. Alongside data clusters, processes for dimension reduction, whose use as pre-processing can increase the generalisation of supervised learning models, will also be covered. In particular, reductions to two dimensions enable visualisations of high-dimensional data. Clustering Basics: kmeans, hierarchical clusters, performance criteria, selection, number of clusters, spectral clustering, visualisation and dimension reduction by means of PCA and other selected methods from the field (Kernel PCA, SOMs, LLE, Isomap, growing neural gas, multidimensional scaling).

Superior module:

Knowledge Discovery and Machine Learning

Module description:

The students know methods for information retrieval and information acquisition from data pools and can apply these approaches independently to pre-processed data (features). They get to know the process in monitored systems for machine learning and are able to use this to resolve classification and regression problems by selecting training and test data, training a model and assessing and optimising its performance. They know the problem of high-dimensional features and corresponding solutions for dimension reduction. In the field of unmonitored learning, they can detect structures in data by means of the cluster process. The students are also able to develop suitable visualisation (high-dimensional) phenomena.

Ethics and Sustainability

Semester 3
Academic year 2
Course code ITBM3ENHIL
Type IL
Kind Compulsory
Language of instruction German
SWS 1
ECTS Credits 1
Examination character immanent

Lecture content:

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

Superior module:

Ethics and Sustainability

Module description:

Graduates are sensitized for dealing with moral and ethical issues in a technical/business context and are prepared for their practical implementation. They can analyse and evaluate ethical values. They know how to integrate various instruments supporting company decision-making processes into the everyday company routine.

Innovation Management

Semester 3
Academic year 2
Course code ITBM3INMIL
Type IL
Kind Compulsory
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Structure of the innovation process and its key indicators, assessment and prioritisation of ideas, further methods such as business model generation, Lean Canvas and Blue Ocean strategies to deepen the ideas of a traditional business plan. Trends such as open innovation, conditions for successful innovation, lean start-up process / "traditional" product development - advantages and disadvantages.

Superior module:

Innovation Management

Module description:

Graduates can apply methods to build up, employ and establish, in the long term, an innovation culture (also regarding software) in the company (from the market for the market); they can analyse and derive relevant information from case studies from science and industry practice. Graduates are able to successfully apply knowledge management in an enterprise.

Intercultural Communication Skills

Semester 3
Academic year 2
Course code ITBM3COSIL
Type IL
Kind Compulsory
Language of instruction English
SWS 1
ECTS Credits 1
Examination character immanent

Lecture content:

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

Superior module:

Communication Skills

Module description:

Using relevant specialist literature, graduates are able to independently work on, orally present and defend a given topic. They can apply the acquired rhetoric skills in discussions and presentations. Graduates have a basic understanding for the complex factors essential to intercultural communication such as stereotypes, different traditions and implications of gestures, intonation or salutations. They are able to recognise and discuss their own culture-based role in the communication context and to consequently deduct the respective actions.

Master Seminar

Semester 3
Academic year 2
Course code ITBM3MASPS
Type PS
Kind Compulsory
Language of instruction German
SWS 1
ECTS Credits 5
Examination character immanent

Lecture content:

Systematic composition of an exposé and discursive defence of the same in group situations; features of a scientific working style; literature phase and thematic breadth (variants); analysis of current publications; dealing with scientific literature sources (also in electronic form) incl. referencing; quality aspects of scientific work.

Superior module:

Master Seminar

Module description:

Graduates are able to independently identify and develop target-oriented research topics for scientific papers. 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.

Pattern Recognition

Semester 3
Academic year 2
Course code ITBM3PRGIL
Type IL
Kind Compulsory
Language of instruction English
SWS 2
ECTS Credits 2.5
Examination character immanent

Lecture content:

Pattern recognition process (data preprocessing, feature extraction, feature reduction, classification); preprocessing and dimension reduction (PCA, Z-score, atan); training, validation and testing data (sampling, cross-validation); Baysian decision theory; mathematical models for the likelihood; types of error and performance analysis (true/false positives and negatives; risk vs. error; spec and sens, ROC); Overview on other methods and algorithms: SVMs, HMMs, NNs; Clustering: hierarchical clustering, partitioning clustering, k-means.

Superior module:

Knowledge Discovery and Machine Learning

Module description:

The students know methods for information retrieval and information acquisition from data pools and can apply these approaches independently to pre-processed data (features). They get to know the process in monitored systems for machine learning and are able to use this to resolve classification and regression problems by selecting training and test data, training a model and assessing and optimising its performance. They know the problem of high-dimensional features and corresponding solutions for dimension reduction. In the field of unmonitored learning, they can detect structures in data by means of the cluster process. The students are also able to develop suitable visualisation (high-dimensional) phenomena.

ELECTIVE: Big Data Engineering

ELECTIVE: Big Data Engineering

Semester 3
Academic year 2
Course code ITBM3BDEIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

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.

Superior module:

ELECTIVE: Big Data Engineering

Module description:

The students know the technical and organisational requirements of Big Data processing and understand methods and techniques for data-intensive software development. They have an overview of the transdisciplinary aspects of Big Data engineering and are familiar with current Big Data frameworks. In addition, they are also able to implement selected case examples of data-intensive applications.

ELECTIVE: Business Leadership

ELECTIVE: Business Leadership

Semester 3
Academic year 2
Course code ITBM3UFGIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Introduction to corporate governance and management, leadership theories, leadership styles, management functions / tasks management process / systems target systems, planning, decision-making, organising, leading, controlling, management processes - application with examples of corporate development; Discourse regarding leadership models (transactional, transformal), leadership theory, fundamentals of leadership, differentiation Leadership and Management, Leadership levels, leadership on team and organisational level, strategic leadership. Outcome assessment, application of performance measurement systems; realisation and chances of success, change management, business development, lean management, human resource management, time management, coordination and conflict management, modern management approaches, foundation management, virtual business, developing a business idea, development of a business plan, implementation of innovative simulation games ("Apollo 13," "target SIM" ).

Superior module:

ELECTIVE: Business Leadership

Module description:

Graduates can identify the structure, interrelationships and the processes within a company. They can identify the management principles and are able to employ the basic instruments of company leadership. They can draft a business plan. They can also implement the different leadership models and related procedures, strengths and weaknesses and differentiations and can assess the effects on corporate culture.

ELECTIVE: Discrete Event Systems

ELECTIVE: Discrete Event Systems

Semester 3
Academic year 2
Course code ITBM3DESIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Introduction to DES system modelling, graphs, finite automata (deterministic, stochastic Markov chains and processes), operating systems, Petri networks: formal validation of the correctness of controllers, analysis of time-dependent Petri networks with MaxPlus algebra for partially synchronised processes; heuristics for process optimisation for operating systems and Smart Grid, specific aspects of distributed control systems; practise: distributed controller modelling per IEC 61499 standard.

Superior module:

ELECTIVE: Discrete Event Systems

Module description:

Graduates are proficient in the methods for modelling discrete events (DES). They learn the mathematical processes for assessing deterministic and stochastic DES and work with DES tools. They understand analysis and implementation methods of new distributed control systems such as Smart Grid.

ELECTIVE: ERP Systems

ELECTIVE: ERP Systems

Semester 3
Academic year 2
Course code ITBM3ERPIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Integration and practical implementation of Enterprise Resource Planning (ERP) into company's existing resources (capital value, equipment or personnel) as efficiently as possible for operational processes and optimisation of the management of business processes. The discussion will differentiate between the technical orientation (target sector), the scalability and the different company sizes (number of required users or company locations). Also the range of functions, the upcoming technologies (databases, programming languages, layered architectures, operating systems) are supported in the practical implementation with a structured approach to a real ERP system; a certification is offered as an option.

Superior module:

ELECTIVE: ERP Systems

Module description:

The students know the structure and interactions/processes within a company. They know the management cycle and are able to employ essential company management instruments. They can create a business plan.

ELECTIVE: Industrial and Medical Imaging

ELECTIVE: Industrial and Medical Imaging

Semester 3
Academic year 2
Course code ITBM3BIVIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Features of industrial image processing and optical quality control, medical imaging modalities (radiography, CT, MRT, ultrasound), medical image data (DICOM) and software tools, image enhancement (grey tone transformations, histogram processing, spatial filtering, Laplace & Gauss filtering), morphological operators (dilation, erosion, closing, opening, hit-or-miss), segmentation (thresholding, adaptive thresholding, Canny, Hough transformation, region growing), form, Fourier and statistical descriptors, Harris detector, basics of interest point detectors and simple features for classification

Superior module:

ELECTIVE: Industrial and Medical Imaging

Module description:

As an introduction to the topic, the students learn the interdisciplinary nature of digital image processing by means of the similarities and differences of industrial and medical image processing. Moving on from the basic stages of an image processing system, they understand the simple, camera-based image recording and also different image-producing processes from the medical world (radiography, CT, MRT, ultrasound). The students acquire a sound knowledge of image enhancement in the time and frequency range and the functionality and application of appropriate filters and methods. The students can likewise apply the concept of morphological image processing, such as the most important algorithms for segmentation or model-based segmentation. Building on that, they know the peculiarities of medical image data (DICOM) and software, as well as the basics of human anatomy required for understanding medical image data. Fundamental concepts for describing shapes, objects as well as the use of simple features for classification round off the course.

ELECTIVE: Parallel Computing

ELECTIVE: Parallel Computing

Semester 3
Academic year 2
Course code ITBM3PACIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Processes and threads, synchronisation of processes and threads, inter-process communication, shared memory, concurrent program, distributed program, concurrent programming, concurrent programming abstraction, consensus; Parallel computing, classification of parallel architectures; Levels of parallelism; Techniques of functional and data decomposition; Parallel systems / hardware; Selected topics in concurrent and parallel computing.

Superior module:

ELECTIVE: Parallel Computing

Module description:

Graduates understand the concepts and challenges of concurrent programming. They are able to apply the tools for formal verification of concurrent computer programs. Furthermore, they can derive parallel algorithms from their sequential form. Graduates are able to measure and analyse the performance of parallel algorithms. They can describe state-of-the-art libraries for concurrent programming on shared memory systems and name physical architectures used for parallel computing.

SPEC: Adaptive Software Systems 2

SPEC: Adaptive Software Systems

Semester 3
Academic year 2
Course code ITBM3ASSIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 6
Examination character immanent

Lecture content:

Adaptive object models and their application; Application integration; vendor-independent generic software platforms; Mobile services and cloud applications; Architectures for migrating applications or software agents; Simulation and test of mobile and migrating applications; Selected topics for software specialists.

Superior module:

SPEC: Adaptive Software Systems 2

Module description:

Graduates show directly employable qualifications in the field of advanced commercial software development and are able to methodically plan, design and implement robust, adaptive and technologically heterogeneous systems and solutions. They have the methodical skills necessary to create industry solutions for fast-changing technical surroundings and to evaluate the fields of application and assess the risks of distributed and autonomous software systems. They can emulate and test mobile and migration-capable applications.

SPEC: Computer Networks and IT-Security 2

SPEC: Protocol Design and Validation

Semester 3
Academic year 2
Course code ITBM3PEVIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 6
Examination character immanent

Lecture content:

The students develop skills for the drafting and performance assessment of transfer protocols. A transport protocol is developed and assessed as a study object within the course of the semester in multiple iterations. Alongside analytical performance assessments and measurements in a real system, the methodical focal point is on discrete event-controlled simulations (DES). The methods learned enable the students to model and assess complex communications systems.

Superior module:

SPEC: Computer Networks and IT-Security 2

Module description:

Graduates can design, specify, test and evaluate the performance of transmission protocols. They can employ simulation methods to fashion and evaluate complex communication systems.

SPEC: Data Science and Analytics 2

SPEC: Deep Learning

Semester 3
Academic year 2
Course code ITBM3DELIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 6
Examination character immanent

Lecture content:

Deep learning paradigms and representation learning; auto-encoder, convolutional neural networks, recurrent neural networks: Theory, parameter and model design, current toolboxes (Tensor Flow, Caffe, CNTK, matlab); implementation in different application domains (image processing, natural language processing, signal processing); optimisation with utilisation of special hardware and software resources (GPU programming, distributed calculation, toolboxes for image processing); the course also offers space to examine current developments.

Superior module:

SPEC: Data Science and Analytics 2

Module description:

The students know approaches and methods from the area of deep learning and representation learning and are able to apply these to data records with suitable toolboxes. In concrete tasks, they investigate the model design and the selection of model parameters and decide on the use of pre-trained models. They also parameterise the respective learning algorithms and apply these to data records with optimum use of hardware and software resources. They are able to use these methods to develop innovative applications and know the limits and areas of use for the respective algorithms.

SPEC: Energy Informatics 2

SPEC: Modeling, Simulation and Optimisation

Semester 3
Academic year 2
Course code ITBM3MSOIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 6
Examination character immanent

Lecture content:

Application of simulation models in the field of demand-response and real-time control. Special focus is given to communication technology models that influence the transient behaviour of energy generation and distribution, integrated simulation tools (co-simulation) for the integration of decentralised energy providers in IP-based communication networks. Demand-response models / optimization through simulation; cascading effects in relation to risks in ICT and energy systems.

Superior module:

SPEC: Energy Informatics 2

Module description:

Graduates can apply relevant methods and algorithms for modelling smart grids to optimise demand-supply as well as demand-response requirements. They can illustrate the complex interplay of the energy and ICT domains in smart grids with respect to real-time control. They can perform risk assessment and evaluate system robustness.

SPEC: Robotics

SPEC: Robot Kinematics

Semester 3
Academic year 2
Course code ITBM3ROKIL
Type IL
Kind Elective
Language of instruction German
SWS 3
ECTS Credits 6
Examination character immanent

Lecture content:

Kinematic configurations and components of industrial robots, spatial transformations, DH-Convention, forward and backward kinematics, velocities and static forces at the end effector, Jacobi matrix, singularities, differential backward kinematics, basics of the Newton-Euler dynamics, practical introduction into robot programming, kinematic simulation in MATLAB.

Superior module:

SPEC: Robotics

Module description:

Graduates can explain the functionality, construction and programming of industrial and mobile robots and implement simple practical tasks. They can apply the principles and basic equations of robot kinematics, especially in the context of forward and inverse kinematics. They are able to plan robot trajectories and control robot movements via external sensors (e.g. image processing systems).

Course titleSWSECTSTYPE

International Economics

Semester 4
Academic year 2
Course code ITBM4BWLIL
Type IL
Kind Compulsory
Language of instruction German
SWS 1
ECTS Credits 2
Examination character immanent

Lecture content:

Current and future-oriented economic phenomena or technologies and their effects on national and international companies are analysed by means of applicable publications; the economic approaches and areas of activity of global corporations are examined, presentation of existing global economic complexes; particular reference is made to features of companies within the European Union.

Superior module:

International Economics

Module description:

Graduates comprehend all aspects of management and marketing and are able to describe modern, market-oriented business enterprises as well as global economic players and they can differentiate their special characteristics.

Master Exam

Semester 4
Academic year 2
Course code ITBM4MAADP
Type DP
Kind Compulsory
Language of instruction German
SWS 0
ECTS Credits 0
Examination character final

Lecture content:

-

Superior module:

Master Thesis

Module description:

Graduates 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. Graduates are able to defend their approach.

Master Thesis

Semester 4
Academic year 2
Course code ITBM4MAAIT
Type IT
Kind Diploma/master thesis
Language of instruction German
SWS 2
ECTS Credits 22
Examination character immanent

Lecture content:

Development and independent processing of the issues and content-related examination for a topic of information technology (the specialisations represent the core technical areas) with particular consideration of the innovation potential of the targeted solutions whilst taking into account a scientifically structured procedure which is also reasoned on the basis of respective current literature.

Superior module:

Master Thesis

Module description:

Graduates 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. Graduates are able to defend their approach.

Targeted Communication

Semester 4
Academic year 2
Course code ITBM4ZOKIL
Type IL
Kind Compulsory
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Identification of subject groups and methods to reach them, forms and framework conditions for effective feedback, exercises and role-playing. Individual processing of the inputs presented and established in the integrative learning course, supported by selective coaching.

Superior module:

Targeted Communication

Module description:

Graduates can discuss relevant economic, social and psychological factors that impact their future professional situation, especially with respect to individual skills profiles and market needs.

ELECTIVE: Sales and Marketing

ELECTIVE: Sales and Marketing

Semester 4
Academic year 2
Course code ITBM4VUMIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Market research methods and their areas of application, marketing mix, product policy, brand policy, pricing policy, distribution policy, national and international distribution strategies, communication policy, Internet marketing, life-cycle marketing, cult-marketing, network marketing, distribution controlling and planning. Practical implementation through Smart Grid case studies, in-depth examination of the sales and marketing thinking and their implementation, analysis of best-practice examples. Environmental factors, market entry strategies, cultures and their influences on market strategies and intercultural negotiations; consideration of ethics in global marketing.

Superior module:

ELECTIVE: Sales and Marketing

Module description:

Graduates know the essential sales and marketing basics and their practical importance. They understand the tools of the marketing mix and their correlations. Graduates know the basics of essential marketing theories and their practical benefits in special situations. They can identify the major tendencies in modern marketing and evaluate their impact on the success of an enterprise. They can deal with complex tasks from different economic fields (case studies), independently solve a problem and document this in a professional manner in English.

ELECTIVE: Smart Grid Business Models

ELECTIVE: Smart Grid Business Models

Semester 4
Academic year 2
Course code ITBM4SGBIL
Type IL
Kind Elective
Language of instruction German
SWS 2
ECTS Credits 3
Examination character immanent

Lecture content:

Energy markets, legislative and political framework conditions, types of energy generation, energy controlling, energy transfer, sales and marketing (MAFO methods, product, branding, pricing, distribution policy, forms of marketing), risk management; consideration of the business models both from the suppliers' perspective and specifically from the users' perspective (user behaviour and user acceptance - „prosumer“). Ethical and sustainability considerations, intercultural aspects. Practical implementation through Smart Grid case studies, analysis of best-practice examples.

Superior module:

ELECTIVE: Smart Grid Business Models

Module description:

Graduates can outline the economic implications of future energy systems in relation to the energy industry and associated markets. They are able to describe the various energy generation, management and distribution possibilities in this context. They can analyse complex scenarios using the case study method and document solutions in English in accordance with domain-specific standards. They can demonstrate the application of innovative smart-grid business models.

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
INTL-CodeIndicates 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
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