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 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.

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.

Input and output modalities geared towards computer systems (screen, keyboard, mouse, but also microphones, loudspeakers, etc.) form the starting point for talking about interaction with computer systems. However, this module deals with a paradigm shift in which the focus is not on operating a computer system (or application), but on enabling the computer system to register and interpret human actions and to take over assistance functions. The computer system itself remains invisible and is integrated into the environment without becoming visible as a technical system. Such systems are becoming increasingly important, particularly due to developments in the Internet of Things, cyber-physical systems and increasing networking.
This module systematically analyzes how direct interaction (e.g. command input) and indirect interactions (e.g. use of context information) differ and how they can be used together to come closer to the vision of an intelligent environment. In addition to the theoretical background, selected aspects from the following areas are also addressed:
Sensor-based interaction technologies
Speech recognition and control
Interactive environments and surfaces
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 are motivated and students are enabled to design and implement such systems themselves.

Technical and methodological competence:
Understand and classify current research work in the field of ambient intelligence.
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 ambient assistance systems.
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 be able to integrate them into their own solutions in a problem-oriented manner.
Create context-sensitive applications by using the context life cycle (measuring, modeling, deriving, distributing).

Interdisciplinary methodological skills:
Identifying alternatives to imperative user interfaces.
Extending applications to intelligent assistance systems.
Evaluate, select and combine forms of interaction.
Deriving semantic information from sensor data.

• Ambient Intelligence (AmI)
• Explicit and implicit interactions in AmI
• New forms of interaction (multimodality, proxemic interaction, tangible computing, affective computing...)
• Context-sensitive applications (context life cycle)
• Semantic modeling of context information
• Context Reasoning (OWL)
• Interaction models for AmI
• Deepening and application in the following technical areas:
  • Sensor-based interaction technologies,
  • Voice recognition and control,
  • Tangible interaction/camera projector systems;
• Ambient environments from the field of AAL, in the task areas:
  • Security & prevention (home emergency call, lighting systems, ),
  • Health and care (vital signs monitoring, fitness trackers, ),
  • Home and care (Google Nest, robotics, service portals, ),
  • Communication and social environment (voice control, communication solutions, );
• AAL platforms and Internet of Things infrastructures as the basis for AmI.
• Approach (analysis, conception, methods, models) for the development of AmI solutions
• Problem solving using the example of a self-developed assistance function from the field of AAL (student projects);

• • Rogers, I. (2012). HCI Theory: Classical, Modern, and Contemporary - Synthesis Lectures on Human-Centered Informatics. Morgen & Claypool.
  • Journal on Multimodal User Interfaces (2016), Volume 10, Springer International Publishing 2016
  • BMBF/VDE Innovationspartnerschaft AAL (Hrsg.) 2011: Ambient Assisted Living (AAL) Komponenten, Projekte, Services Eine Bestandsaufnahme, VDE Verlag.

Technical and methodological competence:

• Processes, activities, methods, techniques, languages and tools for handling IT procurement projects
• Overview of the central procedures, legal framework and relevant tender guidelines for IT procurement projects

Interdisciplinary methodological expertise:

• Requirements management
• Project management
• Market research and analysis

Self-competence:

• Independent preparation and creation of result documents and their presentation on IT procurement-specific topics and content

Social skills:

• Project work in teams with 5-8 students

Professional field orientation:

• Practice-oriented implementation of a tendering and procurement project in cooperation with IT companies

• Project management
  • Project planning with activity node network plans and Gantt charts, cost and effort controlling
• Requirements collection and determination
  • Survey methods such as written surveys and semi-structured interviews with interview guidelines
  • Practical implementation by the project team(s) in cooperation with regional IT companies
• Requirements analysis, specification and documentation
  • Development and creation of requirements documents and functional specifications
  • Outlines and IEEE standards
• Legal framework conditions of an IT procurement project
  • Rights and obligations of the client/contractor
  • ITIL vs. IT procurement
• Structure and preparation of tender documents: forms, regulations, laws
  • EVB-IT, BVB
• Tendering law, public procurement law, tender evaluation
  • Public, restricted and direct award
  • Primary and secondary legal protection
• Conducting bidder interviews and presentations: Process and procedure

• Balzert, H. (2008): Lehrbuch der Softwaretechnik - Softwaremanagement, Heidelberg: Spektrum Akademischer Verlag.
• Balzert, H. (2009): Lehrbuch der Softwaretechnik - Basiskonzepte und Requirements Engineering, 3. Auflage, Heidelberg: Spektrum Akademischer Verlag.
• Keller-Stoltenhoff, Leitzen, Ley (2017): Handbuch für die IT-Beschaffung (Band 1 und 2), Heidelberg: Rehm-Verlag.
• Mangold, P. (2009): IT-Projektmanagement kompakt, 3. erweiterte Auflage, Heidelberg: Spektrum Akademischer Verlag.
• Spitczok, N.; Vollmer, G., Weber-Schäfer, U. (2014): Pragmatisches IT-Projektmanagement, 2. überarbeitete Auflage, Heidelberg: dpunkt-Verlag.
• Vollmer, G. (2018): Vorlesungsunterlagen zur seminaristischen Lehrveranstaltung "Organisatorische und rechtliche Aspekte der IT-Beschaffung"
• Winkelhofer, G. (2005): Management- und Projekt-Methoden, 3. vollst. überarbeitete Auflage, Berlin, Heidelberg: Springer Verlag.

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.

Technical and methodological competence:

• First, central concepts of project management are introduced. In particular, methods of project planning are deepened. Students are able to carry out a planning project.
Students are familiar with current project management standards.The students acquire knowledge of project management methods (in particular time and cost management).Students learn concepts of quality and risk management.

Interdisciplinary methodological skills:

• The students recognize that methods of project management are transferable to other tasks of a business informatics specialist.

Self-competence:

• Selected project management methods are applied by the students themselves during the course
.

Social skills:

• Students learn special methods and tools that support cooperation and communication in a project (e.g. mind mapping, CSCW tools, decision tables, linking of tools. The methods and tools are also used in the course.
The students are able to apply the knowledge from all phases of the course, i.e. to select both methods and tools of project management for this complex project and to apply them in a team.

Professional field orientation:

• The students know the tasks and job description of an IT project manager
.

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

Students learn about work in the field of usability using practical project examples and case studies, as well as current research work from both the practical and theoretical side, apply what they have learned in practice, question the methods used and develop starting points for improvement and further development.

Technical and methodological competence:

• Practical application of common usability engineering tools and methods (AB tests, analysis with GOMS, planning and conducting interviews, tests in the usability lab, remote tests, etc.)
• Evaluation of the tools and procedures for their suitability for a specific project situation
• Classification and assessment of the tools and procedures in the current scientific context
• Adaptation and further development of the tools and procedures for new problems

Self-competence:

• Critical reflection of one's own and others' ways of acting, both in general and in relation to a specific project situation
• Independent development of the current state of research in a defined sub-area

Social skills:

• Developing a communication concept for different target groups (specialist colleagues, different user groups, management levels, etc.)
• Reconciling and coordinating the work in a team
• Observing, recognizing and evaluating behavioural and communication patterns of third parties (e.g. to analyse video recordings during user tests)

Professional field orientation:

• Presentation of the different occupational fields in the field of usability (usability engineer, interface designer, etc.), as an intersection of the disciplines of computer science, business administration, design, work/behavioral sciences)

1. introduction

• Motivation
• Definition of usability engineering
• Link to the course "Human-Computer Interaction"

2. processes

• Usability engineering processes
• Embedding in IT projects
• Potential for conflict
• Communicating usability

3. tools and methods of usability engineering

• Analysis of the context of use
• Determination of the usage requirements
• Concept creation
• Validation

4. industry and application-specific features

In consultation with the students, one to three of the following topics will be covered. The list will be expanded as required

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

Technical and methodological competence:

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

Self-competence:

• Independent processing of current research-related questions

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

Technical and methodological competence:
After successfully completing the module, students know the technical terms of visualization and can use them correctly to describe visualization problems and systems. They will know essential data structures and methods of data visualization. They will be familiar with the architecture of common visualization systems.

Lecture

• Introduction, terminology, history of visualization
• 3D computer graphics
• Visualization process
• Data description for visualization
• Factors influencing the visualization
• Fundamental visualization techniques
• Visualization of multi-parameter data
• Visualization of volume data
• Visualization of flow data
• Visualization systems

Seminar
Presentations on original work from a current international visualization conference, e.g. Eurographics Conference on Visualization

Internship
Testing different paradigms and systems for visualization