Technical and methodological competence:
After completing this course, students will be able to design and develop systems that are predictable in their temporal behavior. They know the technical parameters that are relevant for the selection of planning methods and can select and implement a suitable method based on the advantages and disadvantages. Students will be able to understand the special temporal aspects of synchronized processes and distributed systems in design and implementation.

• Hermann Kopetz. Real-Time Systems: Design Principles for Distributed Embedded Applications, Springer, 2011
• Dieter Zöbel. Echtzeitsysteme Grundlagen der Planung, Springer, 2008.
• Jane Liu. Real-Time Systems, Prentice Hall, 2000.
• Peter Marwedel. Eingebettete Systeme, Springer, 2007.
• Heinz Wörn und Dieter Brinkschulte. Echtzeitsysteme, Springer, 2005.
• Burns, A., Wellings, A.; Real-Time Systems and Programming Languages; Pearson Education Ltd., Third Ed. 2001.

The course is based on the three components of a case study of a HW/SW project during the semester, the preparation of a publication on a current research question and an event with an industry representative. Students acquire the necessary skills to carry out HW/SW projects professionally using current methodology, to adapt and expand the methodology and to present and critically discuss such projects with experts in the field.

• Planning and implementing a development project for a hardware-software system (case study)
• Analyze and assess which processes, methods and tools should be used in such a project (including SystemC, TLM, Mentor Vista Tools)
• Know the model-driven approach and adapt and apply it appropriately in a case study
• Analyze and structure the initial situation (a Viterbi decoder)
• Determine requirements and design the solution and the solution path
• Prepare a publication (+ literature research) for a smaller conference as group work (current research topic in the field of HW/SW codesign, English)

Social skills:

• To work through the case study, the students form project teams and define the roles of the individual team members according to the roles in a HW/SW project (based on Belbin Test)
• Project is planned independently using the methods and processes taught and its implementation is controlled by a project manager
• Project concludes with a lessons learned workshop
• Presentation at the conference (International Research Conference at Fachhochschule Dortmund) for publication (English)

Professional field orientation:

• Presentation and discussion of a practical project by an industry representative
• Students are then able to transfer their knowledge to a practical case and discuss it appropriately
.

Technical and methodological competence:
The student understands the principles, protocols and architecture of computer networks and the applications based on them. He/she applies network design procedures on layer 2 and layer 3, carries out the configuration of network components (router, switch) and plans the setup of virtual networks. He/she understands the design and implementation of communication protocols and is able to design and configure distributed systems with physical and virtual network components.

Social skills:
Based on practical demonstrations and experience gained through practical exercises, he/she is able to evaluate typical and recognized technologies and procedures in the areas of data network communication and the use of virtual network systems.

Technical and methodological competence:

After completing the course, students will be able to

• Classify the concept of the Internet of Things (IoT) and differentiate it from Machine 2 Machine Communication (m2m) and Industry 4.0

• Know the fields of application of IoT and specify their requirements for technology and architecture

• Understand IoT technologies, architectures and protocols and analyze existing IoT systems

• Classify wireless radio technologies such as UWB, LoRaWAN, Z-Wave, ZigBee, Bluetooth Smart in terms of range, data rate, interoperability and power consumption

• Understand routing protocols for ad hoc networking such as OLSR, AODV, DSR and implement them in your own systems

• Select architectures, technologies and protocols for given IoT applications and implement them in your own systems

• Design and implement new architectures and routing protocols for specific IoT applications

• Introduction

  • Motivation, definition, differentiation from m2m, Industry 4.0

  • Application areas and their requirements

  • Overview of layer models: ISO/OSI, TCP/IP, IPv6 and 6LoWPAN, Bluetooth Smart

  • Overview of radio transmission: ISM bands, licensed bands, UWB

  • Classification of technologies: IEEE 802.15.4, Bluetooth Smart, RFID, LoRaWAN

• Architectures and protocols of the IoT

  • Application layer protocols: CoAP, MQTT, GATT

  • Application layer protocol gateways: REST-HTTP/CoAP, REST-HTTP/GATT

  • Topologies: Star and tree topologies with central gateway, mesh networking, multi-gateway

  • Routing protocols: OLSR, AODV, DSR

  • IPv6, 6LoWPAN

• Basics of digital communication

  • Sampling of signals, Nyquist sampling theorem

  • Coding, modulation, Shannon Fano channel capacity

  • Multiple access methods: ALOHA, CSMA/CA, FDMA, TDMA, CDMA, OFDM

  • Radio transmission basics: Antennas, free space attenuation, Fresnel zone,

• Exemplary areas of application

  • Smart Home

    • Scenarios and their requirements

    • Technologies: Z-Wave, ZigBee, EnOcean

    • Exemplary implementation based on a current AAL research project

  • Logistics

    • Scenario Tracking & Tracing

    • Technologies: RFID, LoRaWAN, UWB

    • Exemplary implementation based on a current research project

Jan Höller: From machine-to-machine to the internet of things - introduction to a new age of intelligence, Elsevier, 2014

• Peter Waher: Learning Internet of Things - explore and learn about Internet of Things with the help of engaging and enlightening tutorials designed for Raspberry Pi, Packt Publishing, Birmingham, 2015

• Ralf Gessler, Thomas Krause: Wireless-Netzwerke für den Nahbereich, Eingebettete Funksysteme, ­ Vergleich von standardisierten und proprietären Verfahren, Vieweg+Teubner, 2009

• Martin Meyer: Kommunikationstechnik, Konzepte der modernen Nachrichtenübertragung, Vieweg+Teubner, 4. Auflage, 2011.

• Andrew S. Tanenbaum, David J. Wetherall: Computernetzwerke, 5. Auflage, Pearson Studium, 2012

Technical and methodological competence:

• Students master basic mathematical concepts of computer science and their methods such as set theory, relations, propositional logic, complex numbers as well as groups and solids.
• Students who have completed the module have mastered basic and advanced concepts and methods from linear algebra and are able to apply these methods with reference to their practical applications to solve typical tasks in computer science.
• The graduates demonstrate a confident handling of the concepts and methods of vector and matrix calculus and their geometric interpretation, setting up and solving linear systems of equations as well as dealing with straight lines and planes.

Interdisciplinary methodological skills and self-competence:

• Graduates of the module are able to solve computer science problems by setting up and calculating the corresponding mathematical models (for example by setting up and solving linear systems of equations). They demonstrate confidence in the appropriate selection of problem-specific solution methods and their application.
The students are able to recognize the mathematical structures they have learned in other areas of computer science and to transfer the methods they have learned to these areas.

Social skills:

• The participants understand the relevance of the content taught to their field of study and are able to communicate this relevance adequately.

The event includes the following topics:

• Basics of mathematics for computer scientists: Introduction to set theory, cardinality of sets, relations, basics of propositional logic, complex numbers, groups and solids.
• Vectors and vector calculus: notation and interpretation, operations on vectors and their properties (addition, scalar multiplication, scalar product, cross product), vector spaces, length of vectors, collinearity, linear dependence and independence, concepts of dimension and basis, angles between vectors.
• Lines and planes: Representation in linear algebra, applications, positional relationships between points / straight line / planes
• Matrices: Notation and interpretation, operations on matrices and their properties (transposing matrices, addition, scalar multiplication, matrix multiplication), Gaussian algorithm, determinants, inverse matrices and their calculation
• Linear systems of equations: motivation and applications, matrix-vector form of linear systems of equations, Gaussian algorithm for solving linear systems of equations, homogeneous and inhomogeneous linear systems of equations and their relationships, rank of a matrix and relation to the solution set of linear systems of equations
• Eigenvalues and basic transformations

Teaching the mathematical foundations of quantum computing, insofar as they are relevant to the successful study of computer science. Students should be familiar with the course content listed below and be able to master the central algorithms and assess their significance.

• Handling and calculating with vectors and matrices, especially tensor products, including bra and ket notation
• Know about the historical development and classification in quantum mechanics
• Know and apply quantum teleportation and dense coding
• be able to name the fundamental properties of so-called qubits for quantum computing, describe them in abstract mathematical terms and explain them in terms of their physical principles
• be able to analyze, design and calculate quantum gates, initially simple but then increasingly complex, and implement them in practice with the help of IBM online quantum software
• understand the very abstract quantum Fourier transform after working out the essential properties of the classical Fourier transform, understand it using small examples and be able to apply it
• be able to analyze, understand and apply the essential quantum algorithms (Deutsch, Grover, Shor) and formulate the implications that the real existence of these algorithms will have on future quantum architectures for various application areas
• Know and apply the QFT-based quantum adder as one of the additional application scenarios of the quantum Fourier transform
• Know, apply and evaluate the most important methods of quantum cryptography
  

• B. Lenze, Mathematik und Quantum Computing, Buch und E-Book, Logos Verlag, Berlin, 2020, zweite Auflage.

Ergänzend:

• M. Homeister. Quantum Computing verstehen, Springer Vieweg Verlag, Wiesbaden, 2018, fünfte Auflage.
• R.J. Lipton, K.W. Regan. Quantum Algorithms via Linear Algebra: A Primer, MIT Press, Cambridge MA, 2014.
• M.A. Nielson, I.L. Chuang. Quantum Computation and Quantum Information, Cambridge University Press, Cambridge, 2010.
• C.P. Williams. Explorations in Quantum Computing, Springer-Verlag, London, 2011, zweite Auflage.

Technical and methodological competence:

• Students can explain the specific tasks of managers and differentiate them from specialist tasks.
Students know selected psychological principles of leadership and selected leadership theories.Students are familiar with selected leadership methods and can apply these in case studies and role plays.Students can analyze case descriptions of typical leadership situations and develop and argue solutions based on the theory they have learned.

Interdisciplinary methodological competence:

• The knowledge of psychological principles, the ability to analyze (conflict) situations and communication skills can be used by students in any professional situation.

Social skills:

 

• Group work promotes the ability to develop solutions with other (unfamiliar) students
.
• Role-playing games strengthen skills in dealing constructively with feedback and train the ability to observe communicative (conflict) situations.

Professional field orientation:

• Through guest contributions from HR managers and managers from the field, students learn what requirements are placed on managers in professional fields of computer science.

Students learn about the basic structure of a computer, from simple digital circuits to a typical microprocessor and computer architectures to basic concepts of an operating system.

Technical and methodological competence

• Computer-oriented representation of information (numbers and characters)
• Describing gates and their function, designing simple switching networks, specifying the function of a switching network as a Boolean expression and as a truth table
• Understanding the structure and use of memory elements (selected latches and flip-flops)
• Sketch the structure and basic understanding of how microprocessors and computer architectures work
• Understanding simple machine programs
• Sketch and evaluate simple implementations of the three central tasks of an operating system (process, memory and file management)
• Practical application of the Linux operating system

Social skills

• Solving programming tasks in groups of two
• Presenting the results to the supervisor

Technical and methodological competence:

After completing the course, students will be able to

• define the problem space for new software products or services to be developed and design a solution
• apply the techniques from the field of requirements engineering for the central activities (e.g. elicitation, documentation, validation)
plan requirements engineering processes for specific projects and application domains
• define management activities around requirements
• take the IREB (International Requirements Engineering Board) Foundation Level exam

Social skills:

• Cooperation and teamwork skills are trained during the exercise and project phases. The student can argue in a goal-oriented manner in discussions and deal with criticism objectively; he/she can recognize and reduce existing misunderstandings between discussion partners. Results from group work can be presented together.

Professional field orientation:

• Requirements Engineer / Business Analyst is a designation of a professional field. Participants are able to find a job in this field depending on their field of study
.
• It is a certifiable activity of a computer scientist (IREB).

• Klaus Pohl. Requirements Engineering: Fundamentals, Principles and Techniques. Springer, 2017
• Klaus Pohl und Chris Rupp: Basiswissen Requirements Engineering: Aus- und Weiterbildung nach IREB-Standard zum Certified Professional for Requirements Engineering Foundation Level, 2015
• Brian Berenbach, Daniel Paulish, Juergen Kazmeier, Arnold Rudorfer. Software and Systems Requirements Engineering In Practice, McGraw-Hill, March 2009
• Klaus Pohl, Günter Böckle und Frank J. van der Linden. Software Product Line Engineering: Foundations, Principles and Techniques, Springer, Januar 2011
• Søren Lausen. Software Requirements - Styles and Techniques, Addison-Wesley, 2002.
• Ellen Gottesdiener. Requirements by Collaboration - Workshops for Defining Needs. Addison-Wesley, 2002

Technical and methodological competence:

• The students should
• know and be able to classify quality terms
• be able to explain and justify the principles of software quality assurance
• Be able to carry out (code) inspections
• be able to analyze programs and use control-flow-oriented and data-flow-oriented test procedures
• be able to use the concepts of verification and symbolic testing and differentiate them from testing procedures
• be able to carry out integration and acceptance tests for simple scenarios
• Be able to assess and use test tools
• Be able to determine and use tools and procedures for test automation

Interdisciplinary methodological competence:

• Learning quality management methods that are transferable to other areas beyond the field of software development
.

Self-competence:

• Independent familiarization with in-depth questions and presentation of results

Social skills:

• Independent development of exercise units, practice with fellow students, organization of feedback by fellow students

Technical and methodological competence:

• Be able to name basic terms and properties of formal languages, grammars and the corresponding automata
.
• Create grammars and automata for formal languages and understand how they work.
• Be able to convert the representation of languages between grammars, automata and regular expressions.
Be able to independently assess problems as formal languages and classify them with regard to the language types in the Chomsky hierarchy.

Interdisciplinary methodological competence:

• Be able to independently assess and classify problems in terms of their complexity
.

Ability to extract information from data using statistical methods, especially regression methods.

Technical and methodological competence:

• Acquisition of methodological knowledge of explorative and inductive statistics
• Formulating statistical models, especially regression models
• Selection and implementation of parameter estimation, model selection, model testing with subsequent interpretation of results
• Calculating forecasts and forecast intervals
• Conducting and analyzing real experiments and computer simulations based on statistical experimental design
• Model-based optimization of technical and logistical processes
• Independent analysis of data sets with statistical software (R, JMP,...) and documentation in report form

Interdisciplinary methodological competence:

• Supporting decision-making processes through data analysis
• Creating forecasts with uncertainty estimation based on data sets
• Applying statistical methods in connection with the evaluation of databases

• Definition of the classical linear model
• Model parameters, ML and KQ estimation
• Hypothesis testing in the context of regression models
• Residual analysis
• Model selection and variable selection
• Model interpretation, forecasting and forecasting intervals
• Basics of statistical experimental design (experimental design, experimental range, coding, randomization, repetitions, block formation)
• Screening and optimization plans, space-filling plans
• Insight into various statistical models (analysis of variance, generalized linear models, Gaussian process models, )

In the course "Selected Aspects of Practical Computer Science", content on a special topic of practical computer science is presented.

This course offers the opportunity to offer a course that is not offered on a regular annual basis, and lecturers from Germany and abroad and cooperation partners can be approached to present interesting aspects.

The topics offered specifically expand the range of courses in the field of practical computer science.

Technical and methodological skills

• The students know the basics of the topic
• The students know the requirements, principles, architectures, methods, procedures and tools for the topic
• The students can work independently on tasks (case studies, project tasks, development tasks)
.

Self-competence

• Students develop their results independently or in teams and present them
.

Social competence:

• Practical work is carried out in teams
.

In this course, a lecturer will specifically present 'Selected Aspects of Practical Computer Science'

This course is offered in coordination with the Dean of Studies, taking capacity aspects into account.

A module description - in accordance with the specifications in the module handbook - is created in advance for the specific course. The head of degree program uses this to check the suitability of the course to complement the curriculum. The module description is made available to the students from the beginning of the course.

In the course "Selected Aspects of Computer Engineering", content on a special topic of computer engineering is presented.

This course offers the opportunity to offer a course that is not offered on a regular annual basis, and lecturers from Germany and abroad and cooperation partners can be approached to present interesting aspects.

The topics offered specifically expand the range of courses in the field of computer engineering.

Technical and methodological skills

• The students know the basics of the topic
• The students know the requirements, principles, architectures, methods, procedures and tools for the topic
• Students can work independently on topic-specific tasks (case studies, project tasks, development tasks)
.
• Validate!!!

Self-competence

• Students develop their results independently or in teams and present them
.

Social competence:

• Practical work is carried out in teams
.

In this course, a lecturer will specifically present 'Selected Aspects of Computer Engineering'

This course is offered in coordination with the Dean of Studies due to capacity considerations.

A module description is created in advance for the specific course - in accordance with the specifications of the module handbook - and made available to the students. Quality assurance is carried out by the head of degree program.

Technical and methodological competence:

After completing the course, students will be able to

• understand and apply methods and algorithms of autonomous mobile systems
• design and implement state controllers and state observers
• apply algorithms for state estimation of dynamic systems
• Apply and implement algorithms for localization, path planning and collision avoidance of autonomous mobile systems

Interdisciplinary methodological competence:

• Analysis of dynamic systems
• Mathematical modeling of dynamic systems
• Simulation of dynamic systems with Matlab/Simulink

Technical and methodological competence:

• Be able to name basic terms and concepts of computability and complexity theory
.
• Be able to program and analyze different models of Turing machines.
• Understand, classify and evaluate complexity statements of problems.
Be able to independently assess and classify problems in terms of their computability and complexity.Be able to check the possibility of an approximate solution for difficult problems.

Teaching advanced content on the topic of distributed systems and teaching the basics of wireless and mobile systems

Technical and methodological skills:

• Describing the basics of signal propagation and transmission techniques
• Naming and describing the most important technologies (wired and wireless)
• Differentiated description of the special aspects of routing, QoS and localization
• Understanding the special features of software development for small devices (e. g.B. smartphones) in detail
• Classify current and future developments in the overall context
• Perform prototype programming of wireless applications

Self-competence:

• Dealing independently with current research-related issues

Social skills:

• Working in small teams
• Results-oriented group work

Literatur:

• Schiller, Jochen: Mbilkommunikation, Pearson Studium, 2000
• Sauter, Martin: Grundkurs Mobile Kommunikationssysteme: UMTS, HSDPA und LTE, GSM, GPRS und Wireless LAN, Vieweg und Teubner, 4. Auflage 2011
• Firtman, M.: Programming the Mobile Web, O'Reilly Media, 2010
• Fling, B.: Mobile Design an Development: Practical Concepts and Techniques for Creating Mobile Sites and Web Apps, O'Reilly Media, 2010

In this module, students deepen their skills in the design of software architectures for complex systems. Students learn how to design a scalable, robust and maintainable domain-driven software architecture by selecting and applying suitable principles, patterns and methods. The analysis and discussion of such software architectures is based on practical examples and concrete solutions from research projects.

Technical and methodological competence:

• The participants are able to differentiate basic principles of software design and transfer them to specific application scenarios.
The students are able to differentiate, analyze and apply central patterns at the macro and micro architecture level.The students know relevant tools and methods for domain-driven design and can combine and implement them appropriately in concrete application scenarios.The students can name and classify current research approaches to modeling software architectures.

Interdisciplinary methodological competence:

• The participants master the analysis of a complex problem and can break it down into sub-problems. In this way, they strengthen their skills in implementing a comprehensive task as part of a project over several weeks in a team.
Students learn methods for the interdisciplinary development of solutions, e.g. together with experts without a technical background.

Social skills:

• The participants develop and implement solutions cooperatively in a team
.
• They are also able to present, explain and discuss their ideas and solutions.

Professional field orientation:

• Students acquire knowledge to solve typical tasks in the field of software architectures. They can make well-founded design decisions and justify them.
• In addition, they gain experience in the use of essential software development tools, such as development environments or build management tools.

The module covers the following topics:

• Short repetition of the bachelor material on software design (e.g. design patterns according to Gamma et al., separation of concerns, layered architecture)
• In-depth aspects of software design:
  • Principles (e.g. loose coupling - high cohesion, SOLID)
  • Architectural patterns (e.g. ports and adapters, CQRS)
  • Methods (e.g. domain-driven design, WAM approach)
• Characteristics and patterns of modern architectural styles (e.g. modular architectures, event-based architectures, microservice architectures)
• Model-driven design, development and reconstruction of software architectures

• Evans E.; Domain-Driven Design: Tackling Complexity in the Heart of Software. Addison-Wesley; 2003
• Vernon V.; Domain-Driven Design kompakt. dpunkt; 2017
• Richardson C.; Microservice Patterns. Manning; 2018
• Starke G.; Effektive Softwarearchitekturen. Hanser Verlag; 8. Auflage; 2018
• Martin R. C.; Clean Architecture. Prentice Hall; 2018
• Goll J.; Entwurfsprinzipien und Konstruktionskonzepte der Softwaretechnik. Prentice Hall; Springer Vieweg; 2018
• Bass, Len, Paul Clements, and Rick Kazman. Software Architecture in Practice: Software Architect Practice. Addison-Wesley, 2012.
• Balzert H.; Lehrbuch der Softwaretechnik. Entwurf, Implementierung, Installation und Betrieb. Spektrum Akademischer Verlag; 3. Auflage; 2011
• Gamma E., Helm R., Johnson R., Vlissides J.; Design Patterns. Addison-Wesley; 1995
• Rademacher, Florian. A language ecosystem for modeling microservice architecture. Diss. 2022.

After completing the course, students will be able to xxx

Subject and methodological competence:

• identify problems that should be solved using compiler construction techniques
• to specify the grammars that generate simple formal languages and to verify the result
• explain the relationship between tokens, regular expressions, regular languages and the automata that accept them and, based on this knowledge, develop the automaton for a token that accepts exactly the lexemes belonging to the token
• develop a scanner for a small example language
• to decide for a given grammar whether it is suitable for dead-end-free top-down analysis and, if necessary, to modify problematic productions appropriately
• develop a parser for a small example language based on recursive descent
• extend small grammars with suitable attributes and semantic rules for the purpose of syntax-driven translation
• to develop a syntax-driven translator on the basis of predefined translation schemes
• make suitable decisions for memory organization and runtime system based on a source language to be translated
• name the components of an abstract 3-address machine
• name common optimization methods and apply them to given 3-address code

In this module, students gain an overview of the architectures of complex web applications and analyze their differences and areas of application. They learn how corresponding web applications can be implemented by selecting and using suitable client- and server-side technologies.

• The participants can analyze and differentiate between different architectures and central architectural patterns of web applications
The participants are able to derive a suitable architecture from a concrete problem and to determine and apply suitable web technologies for implementation.Students will be able to name, classify and apply important web standards and technologies.

Interdisciplinary methodological competence:

• The participants have mastered the analysis of a comprehensive requirement and can break it down into sub-requirements. They have experience of implementing sub-requirements over several weeks as part of an overall project in a team.
Students can classify, derive and implement software system architectures.

Social skills:

• The participants develop and implement solutions cooperatively in a team
.
• They are also able to present, explain and discuss their ideas and solutions.


Professional field orientation:

• Students acquire knowledge of typical tasks in web development and the application of specific web technologies.
In addition, they gain experience in the use of essential software development tools, such as development environments or build management tools.

The lecture covers the following topics:

• Brief review of the basics of building websites with HTML, CSS and JavaScript (Bachelor material)
• Consideration, analysis and differentiation of architectures of modern web applications:
  • Architectural patterns such as MVC and its variants (MVVM, MVP, etc.)
  • Request-based and component-based web frameworks
  • Single page applications, server-side rendering, client-side rendering
  • Reactive programming/streaming
• In-depth study of server-side technologies for the development of web applications (e.g. with Java, JavaScript)
• Deepening client-side concepts and technologies for the development of web applications (e.g. component-oriented development, state management, routing)
• Overview of current developments in web standards (e.g. web components, WebAssembly)

• Wolf J.; HTML5 und CSS3: Das umfassende Handbuch; Rheinwerk Computing; 4. Auflage; 2021
• Bühler P., Schlaich P., Sinner D.; HTML5 und CSS3: Semantik - Design- Responsive Layouts; Springer Vieweg; 2017
• Simpson K.; Buchreihe "You Don't Know JS" (6 Bände); O'Reilly; 2015
• Haverbeke M.; JavaScript : richtig gut programmieren lernen; dpunkt.verlag; 2. Auflage; 2020
• Simons M.; Spring Boot 2: Moderne Softwareentwicklung mit Spring 5; dpunkt.verlag; 2018
• Tilkov S., Eigenbrodt M., Schreier S., Wolf O.; REST und HTTP; dpunkt.verlag; 3. Auflage; 2015
• Kress, D.; GraphQL: Eine Einführung in APIs mit GraphQL; dpunkt.verlag; 2020
• Starke G.; Effektive Softwarearchitekturen. Hanser Verlag; 9. Auflage; 2020

In the last ten years, the approach of taking a very pragmatic view of programming languages in introductory programming lectures has become widespread. In essence, the syntax of a specific programming language is taught (e.g. Java) and it is shown how specific tasks can be solved using the language. Individual concepts such as data types and control structures are primarily illustrated using concrete examples and not discussed in general terms.

The course deals with the development and analysis of machine learning methods in applications of computer science, medical informatics and general information systems.

Technical and methodological competence:

After attending the course, students will be able to

• use the most important concepts of machine learning to explain learning systems
.
• design, implement and analyze machine learning systems for specific applications in computer science
• assess the use of machine learning methods for their own application tasks. To this end, students are familiar with typical applications for these methods.
recognize the theoretical limits of machine learning systems, describe them formally and use them to assess the limits of their own applications.question and discuss the ethical foundations of machine learning systems.

Self-competence:

The student can:

• formulate ideas and proposed solutions in writing and orally
• solve tasks independently in the exercises and practicals and present the results
• acquire theoretical content on the topic of machine learning from scientific literature and present it independently

 

Social skills:

The student can:

• Develop solutions cooperatively in the exercise and project phases
• plan, distribute and jointly carry out tasks for solutions in the project phases
• argue in a goal-oriented manner in discussions and deal with criticism objectively
• Present the results of group work together
• Evaluate project results and formulate suggestions for improvement
• Recognize and reduce existing misunderstandings between discussion partners

 

• I. Witten, E. Frank, M. Hall und C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4. Auflage, Morgan Kaufmann (2017) - elektronische Version im Intranet verfügbar
• C. M. Bishop, Pattern Recognition and Machine Learning, Springer (2006)
• E. Alpaydin, Introduction to Machine Learning (Adaptive Computation and Machine Learning), Third Edition, MIT Press (2014)
• I. Goodfellow, Y. Bengio und A. Courville: Deep Learning, MIT Press (2016)

Teaching the mathematical fundamentals of encryption technology, insofar as they are relevant to the successful study of computer science. Students should be familiar with the course content listed below and be able to make informed decisions about which encryption technique to use to solve which encryption problem.

Technical and methodological competence:

• Dealing with and calculating in groups, rings and solids
• Polynomial and dual arithmetic in Galois fields
• Knowing and applying the extended Euclidean algorithm, the Chinese remainder theorem and Fermat's and Euler's theorem
• Name important one-way functions (with and without trapdoor) and know their essential properties
• Know, apply and evaluate Diffie-Hellman and RSA methods, Vernam, DES and AES methods as well as the most important ECC methods
• be able to name, apply and systematically compare all common asymmetric and symmetric encryption methods and assess their security
• be able to describe and analyze the basics of standard encryption methods as well as the two post-quantum encryption methods presented on an abstract mathematical level
• be able to propose, justify, analyze and critically assess alternatives, modifications and, in the best case, improvements based on the insights gained into the methods presented
• be able to easily familiarize themselves with other, not explicitly presented procedures on the basis of the solid theory developed and also systematically compare and assess them with regard to their safety

• B. Lenze, Basiswissen Angewandte Mathematik -- Numerik, Grafik, Kryptik --, Buch und E-Book, Springer Vieweg Verlag, Wiesbaden, 2020, zweite Auflage.

Ergänzend:

• D.J. Bernstein, J. Buchmann, E. Dahmen, Post-Quantum-Cryptography, Springer-Verlag, Berlin-Heidelberg, 2009.
• J. Buchmann, Einführung in die Kryptographie, Springer-Verlag, Berlin-Heidelberg-New York, 2016, sechste Auflage.
• H. Delfs, H. Knebl, Introduction to Cryptography, Springer-Verlag, Berlin-Heidelberg, 2015, dritte Auflage.
• J. Hoffstein, J. Pipher, J.H. Silverman, An Introduction to Mathematical Cryptography, Springer-Verlag, New York, 2014, zweite Auflage.
• C. Paar, J. Pelzl, Kryptografie verständlich, Springer Vieweg Verlag, Berlin-Heidelberg, 2016.
• D. Wätjen, Kryptographie, Springer Vieweg Verlag, Wiesbaden, 2018, dritte Auflage.
• A. Werner, Elliptische Kurven in der Kryptographie, Springer-Verlag, Berlin-Heidelberg-New York, 2013.

This module focuses on interaction with multimodal user interfaces. It deals with modern forms of human-machine interaction (MMI) in networked intelligent environments. In addition to the theoretical background, a section of the following areas will be addressed:

- Speech recognition and control

- Interactive environments and interfaces

- Ambient environments

- Physiological sensors for interaction (affective computing)

- Tangible interaction (tangible interaction, physical computing)

- Goal-based interaction

In the application field of Ambient Assisted Living, concepts, methods and technologies of modern MMI are motivated and practically deepened in the course of the event

Technical and methodological expertise:

After attending the course, students will be able to

• Understand and evaluate current research work in the field of multimodal interaction systems
.
• Understand and analyze new (sensor-based, tangible, voice-based, ) forms of interaction and transfer them to their own use cases. To this end, students are familiar with typical areas of application and are able to classify technologies and infrastructures.
apply concepts, methods and models for the development of multimodal user interfaces.recognize requirements (especially for the MMI) of modern AAL systems and assemble solutions/products in their context as building blocks of a problem solution.Understand infrastructures for new forms of interaction and develop their own solutions based on the problem at hand.Analyze existing multimodal user interfaces for specific use cases and develop new ones.

Self-competence:

Students can present ideas and proposed solutions in writing and orally, the independent presentation of solutions contributes to the development of self-confidence/professional competence; the development of strategies for the acquisition of knowledge and skills is supported by the combination of (seminar-style) lectures with independent development of the contents of scientific literature.

Social competence:

Cooperation and teamwork skills are trained during the exercise and project phases. The student can argue in a goal-oriented manner in discussions and deal with criticism objectively; he/she can recognize and reduce existing misunderstandings between discussion partners. Results from group work can be presented together.