The students know the important basics of discrete algebraic structures including elementary combinatorial structures, monoids, groups, rings, fields, finite fields, and vector spaces. They also know specific structures like sub-. sum-, and quotient structures and homomorphisms. 

Students are able to formalize and analyze basic discrete algebraic structures.

Students are able to solve specific problems alone or in a group and to present the results accordingly.

Students apply the principles, constructs, and simple design techniques of functional programming. They demonstrate their ability to read Haskell programs and to explain Haskell syntax as well as Haskell's read-eval-print loop. They interpret warnings and find errors in programs. They apply the fundamental data structures, data types, and type constructors. They employ strategies for unit tests of functions and simple proof techniques for partial and total correctness. They distinguish laziness from other evaluation strategies. 

Students break a natural-language description down in parts amenable to a formal specification and develop a functional program in a structured way. They assess different language constructs, make conscious selections both at specification and implementations level, and justify their choice. They analyze given programs and rewrite them in a controlled way. They design and implement unit tests and can assess the quality of their tests. They argue for the correctness of their program.

Students practice peer programming with varying peers. They explain problems and solutions to their peer. They defend their programs orally. They communicate in English.

The Non-technical Academic Programms (NTA)

imparts skills that, in view of the TUHH’s training profile, professional engineering studies require but are not able to cover fully. Self-reliance, self-management, collaboration and professional and personnel management competences. The department implements these training objectives in its teaching architecture, in its teaching and learning arrangements, in teaching areas and by means of teaching offerings in which students can qualify by opting for specific competences and a competence level at the Bachelor’s or Master’s level. The teaching offerings are pooled in two different catalogues for nontechnical complementary courses.

The Learning Architecture

consists of a cross-disciplinarily study offering. The centrally designed teaching offering ensures that courses in the nontechnical academic programms follow the specific profiling of TUHH degree courses.

The learning architecture demands and trains independent educational planning as regards the individual development of competences. It also provides orientation knowledge in the form of “profiles”

The subjects that can be studied in parallel throughout the student’s entire study program - if need be, it can be studied in one to two semesters. In view of the adaptation problems that individuals commonly face in their first semesters after making the transition from school to university and in order to encourage individually planned semesters abroad, there is no obligation to study these subjects in one or two specific semesters during the course of studies.

Teaching and Learning Arrangements

provide for students, separated into B.Sc. and M.Sc., to learn with and from each other across semesters. The challenge of dealing with interdisciplinarity and a variety of stages of learning in courses are part of the learning architecture and are deliberately encouraged in specific courses.

Fields of Teaching

are based on research findings from the academic disciplines cultural studies, social studies, arts, historical studies, migration studies, communication studies and sustainability research, and from engineering didactics. In addition, from the winter semester 2014/15 students on all Bachelor’s courses will have the opportunity to learn about business management and start-ups in a goal-oriented way.

The fields of teaching are augmented by soft skills offers and a foreign language offer. Here, the focus is on encouraging goal-oriented communication skills, e.g. the skills required by outgoing engineers in international and intercultural situations.

The Competence Level

of the courses offered in this area is different as regards the basic training objective in the Bachelor’s and Master’s fields. These differences are reflected in the practical examples used, in content topics that refer to different professional application contexts, and in the higher scientific and theoretical level of abstraction in the B.Sc.

This is also reflected in the different quality of soft skills, which relate to the different team positions and different group leadership functions of Bachelor’s and Master’s graduates in their future working life.

Specialized Competence (Knowledge)

Students can

• locate selected specialized areas with the relevant non-technical mother discipline,
• outline basic theories, categories, terminology, models, concepts or artistic techniques in the disciplines represented in the learning area,
• different specialist disciplines relate to their own discipline and differentiate it as well as make connections, 
• sketch the basic outlines of how scientific disciplines, paradigms, models, instruments, methods and forms of representation in the specialized sciences are subject to individual and socio-cultural interpretation and historicity,
• Can communicate in a foreign language in a manner appropriate to the subject.

Professional Competence (Skills)

In selected sub-areas students can

• apply basic methods of the said scientific disciplines,
• auestion a specific technical phenomena, models, theories from the viewpoint of another, aforementioned specialist discipline,
• to handle simple questions in aforementioned scientific disciplines in a sucsessful manner,
• justify their decisions on forms of organization and application in practical questions in contexts that go beyond the technical relationship to the subject.

Personal Competences (Social Skills)

Students will be able

• to learn to collaborate in different manner,
• to present and analyze problems in the abovementioned fields in a partner or group situation in a manner appropriate to the addressees,
• to express themselves competently, in a culturally appropriate and gender-sensitive manner in the language of the country (as far as this study-focus would be chosen), 
• to explain nontechnical items to auditorium with technical background knowledge.

Students will know

        - the essential features of a procedural programming language
        - the steps during the compilation of procedural source code to machine code
        - all essential language constructs and data types of a procedural programming language
        - software design concepts for the implementation of procedural programs

       - Mastery of typical development tools  
       - Designing simple, structured programs based on a procedural programming language
       - Debugging by analyzing compiler warnings and error messages
       - Analysis and explanation of procedural programs

        - After completing the module, students are able to work on subject-specific tasks alone or in a group and to present the results appropriately.
        

• Students can name the basic concepts in analysis and linear algebra. They are able to explain them using appropriate examples.
• Students can discuss logical connections between these concepts.  They are capable of illustrating these connections with the help of examples.
• They know proof strategies and can reproduce them.

• Students can model problems in analysis and linear algebra with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods.
• Students are able to discover and verify further logical connections between the concepts studied in the course.
• For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.

• Students are able to work together in teams. They are capable to use mathematics as a common language.
• In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

Students can explain syntax, semantics, and decision problems of propositional logic, and they are able to give algorithms for solving decision problems. Students can show correspondences to Boolean algebra. Students can describe which application problems are hard to represent with propositional logic, and therefore, the students can motivate predicate logic, and define syntax, semantics, and decision problems for this representation formalism. Students can explain unification and resolution for solving the predicate logic SAT decision problem. Students can also describe syntax, semantics, and decision problems for various kinds of temporal logic, and identify their application areas. The participants of the course can define various kinds of finite automata and can identify relationships to logic and formal grammars. The spectrum that students can explain ranges from deterministic and nondeterministic finite automata and pushdown automata to Turing machines. Students can name those formalism for which nondeterminism is more expressive than determinism. They are also able to demonstrate which decision problems require which expressivity, and, in addition, students can transform decision problems w.r.t. one formalism into decision problems w.r.t. other formalisms. They understand that some formalisms easily induce algorithms whereas others are best suited for specifying systems and their properties. Students can describe the relationships between formalisms such as logic, automata, or grammars.

Students can apply propositional logic as well as predicate logic resolution to a given set of formulas. Students analyze application problems in order to derive propositional logic, predicate logic, or temporal logic formulas to represent them. They can evaluate which formalism is best suited for a particular application problem, and they can demonstrate the application of algorithms for decision problems to specific formulas. Students can also transform nondeterministic automata into deterministic ones, or derive grammars from automata and vice versa. They can show how parsers work, and they can apply algorithms for the language emptiness problem in case of infinite words.

• Students are able to work together in teams. They are capable to use mathematics as a common language.
• In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

After taking this module, students know the important basics of many different areas in Business and Management, from Planning and Organisation to Marketing and Innovation, and also to Investment and Controlling. In particular they are able to

• explain the differences between Economics and Management and the sub-disciplines in Management and to name important definitions from the field of Management
• explain the most important aspects of and goals in Management and name the most important aspects of entreprneurial projects 
  
• describe and explain basic business functions as production, procurement and sourcing, supply chain management, organization and human ressource management, information management, innovation management and marketing 
  
• explain the relevance of planning and decision making in Business, esp. in situations under multiple objectives and uncertainty, and explain some basic methods from mathematical Finance 
• state basics from accounting and costing and selected controlling methods.

Students are able to analyse business units with respect to different criteria (organization, objectives, strategies etc.) and to carry out an Entrepreneurship project in a team. In particular, they are able to

• analyse Management goals and structure them appropriately
• analyse organisational and staff structures of companies
  
• apply methods for decision making under multiple objectives, under uncertainty and under risk
• analyse production and procurement systems and Business information systems
• analyse and apply basic methods of marketing
• select and apply basic methods from mathematical finance to predefined problems
  
• apply basic methods from accounting, costing and controlling to predefined problems
  
  

Students are able to

• work successfully in a team of students
• to apply their knowledge from the lecture to an entrepreneurship project and write a coherent report on the project
• to communicate appropriately and
• to cooperate respectfully with their fellow students. 
  

The students have a fundamental understanding of object orientated and generic programming and can apply it in small programming projects. The can design own class hierarchies and differentiate between different ways of inheritance. They have a fundamental understanding of polymorphism and can differentiate between run-time and compile-time polymorphism. The students know the concept of information hiding and can design interfaces with public and private methods. They can use exceptions and apply generic programming in order to make existing data structures generic. The students know the pros and cons of both programming paradigms.

Students can break down a medium-sized problem into subproblems and create their own classes in an object-oriented programming language based on these subproblems. They can design a public and private interface and implement the implementation generically and extensible by abstraction. They can distinguish different language constructs of a modern programming language and use these suitably in the implementation. They can design and implement unit tests.

Students can work in teams and communicate in forums.

• Students can name the basic concepts in analysis and linear algebra. They are able to explain them using appropriate examples.
• Students can discuss logical connections between these concepts.  They are capable of illustrating these connections with the help of examples.
• They know proof strategies and can reproduce them.

• Students can model problems in analysis and linear algebra with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods.
• Students are able to discover and verify further logical connections between the concepts studied in the course.
• For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.

• Students are able to work together in teams. They are capable to use mathematics as a common language.
• In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

Students are able to explain important and common Internet protocols in detail and classify them, in order to be able to analyse and develop networked systems in further studies and job.

Students are able to analyse common Internet protocols and evaluate the use of them in different domains.

This module deals with the foundations of the functionality of computing systems. It covers the layers from the assembly-level programming down to gates. The module includes the following topics:

• Introduction
• Combinational logic: Gates, Boolean algebra, Boolean functions, hardware synthesis, combinational networks
• Sequential logic: Flip-flops, automata, systematic hardware design
• Technological foundations
• Computer arithmetic: Integer addition, subtraction, multiplication and division
• Basics of computer architecture: Programming models, MIPS single-cycle architecture, pipelining
• Memories: Memory hierarchies, SRAM, DRAM, caches
• Input/output: I/O from the perspective of the CPU, principles of passing data, point-to-point connections, busses
  

The students perceive computer systems from the architect's perspective, i.e., they identify the internal structure and the physical composition of computer systems. The students can analyze, how highly specific and individual computers can be built based on a collection of few and simple components. They are able to distinguish between and to explain the different abstraction layers of today's computing systems - from gates and circuits up to complete processors.

After successful completion of the module, the students are able to judge the interdependencies between a physical computer system and the software executed on it. In particular, they shall understand the consequences that the execution of software has on the hardware-centric abstraction layers from the assembly language down to gates. This way, they will be enabled to evaluate the impact that these low abstraction levels have on an entire system's performance and to propose feasible options.

Students are able to solve similar problems alone or in a group and to present the results accordingly.

After successful completion of the course, students know:

• Introduction to database systems
• Design instruments for relational databases, especially entity-relationship
• The relational model
• Relational query languages, especially SQL
• Normalization
• Physical data organization
• Transaction management
• Query optimization
• Data representation
• Object-oriented and object-relational databases
• Paradigms and concepts of current technologies for data modelling and database systems

The students acquire the ability to model a database and to work with it. This comprises especially the application of design methodologies and query and definition languages. Furthermore, students are able to apply basic functionalities needed to run a database.

Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.

• Students can name the basic concepts in the area of analysis and differential equations. They are able to explain them using appropriate examples.
• Students can discuss logical connections between these concepts.  They are capable of illustrating these connections with the help of examples.
• They know proof strategies and can reproduce them.

• Students can model problems in the area of analysis and differential equations with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods.
• Students are able to discover and verify further logical connections between the concepts studied in the course.
• For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.

• Students are able to work together in teams. They are capable to use mathematics as a common language.
• In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

• Students can name the basic concepts in algorithm design, algorithm analysis and problem reductions. They are able to explain them using appropriate examples.
• Students can discuss logical connections between these concepts.  They are capable of illustrating these connections with the help of examples.
• They know proof strategies and can reproduce them.

• Students can model discrete decision, search and optimization problems with the help of the concepts studied in this course. Moreover, they are capable of solving them, and reducing them to each other, by applying established methods.
• Students are able to discover and verify further logical connections between the concepts studied in the course.
• For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.

• Students are able to work together in teams. They are capable to use mathematics as a common language.
• In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

Students explain the phases of the software life cycle, describe the fundamental terminology and concepts of software engineering, and paraphrase the principles of structured software development. They give examples of software-engineering tasks of existing large-scale systems. They write test cases for different test strategies and devise specifications or models using different notations, and critique both. They explain simple design patterns and the major activities in requirements analysis, maintenance, and project planning.

For a given task in the software life cycle, students identify the corresponding phase and select an appropriate method. They choose the proper approach for quality assurance. They design tests for realistic systems, assess the quality of the tests, and find errors at different levels. They apply and modify non-executable artifacts. They integrate components based on interface specifications.

Students practice peer programming. They explain problems and solutions to their peer. They communicate in English. 

• Students can name the basic concepts in Stochastics. They are able to explain them using appropriate examples.
• Students can discuss logical connections between these concepts.  They are capable of illustrating these connections with the help of examples.
• They know proof strategies and can reproduce them.

• Students can model problems from stochastics with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods.
  
• Students are able to discover and verify further logical connections between the concepts studied in the course.
• For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.

• Students are able to work together (e.g. on their regular home work) in heterogeneously composed teams (i.e., teams from different study programs and background knowledge) and to present their results appropriately (e.g. during exercise class).

• In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

• Basic models of computation (finite state machines, Turing machines)
• Decision problems and formal languages
• Gödel numbering of computations
• Universal computability
• Decidable and undecidable problems
• Reductions, diagonalization, Rice’s theorem
• Time and space complexity
• The complexity classes P and NP
• Hierarchy theorems
• Polynomial time reductions, NP-completeness
• Cook-Levin theorem
• Uniform circuit families

After completing this module, students are able to 

• reproduce the knowledge taught in the course,
• reproduce simpler proofs of the course and reproduce the ideas of the more complicated ones,
• establish connections between the concepts taught, and
• apply the learned knowledge to concrete problems.

After completing this module, students are able to work on subject-specific tasks alone or in a group and to present the results appropriately.

• Students can name the basic concepts in Graph Theory and Optimization. They are able to explain them using appropriate examples.
• Students can discuss logical connections between these concepts.  They are capable of illustrating these connections with the help of examples.
• They know proof strategies and can reproduce them.

• Students can model problems in Graph Theory and Optimization with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods.
• Students are able to discover and verify further logical connections between the concepts studied in the course.
• For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.

• Students are able to work together in teams. They are capable to use mathematics as a common language.
• In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

Students know the important aspects and phases of software development.

Students can describe the typical phases of software development and are able to contribute to a software project.

Students are able to specify, implement, and analyze specific basic topics in software development and present them accordingly.

The students are able to

• explicate a specific topic in the field of Computer Science,
• describe complex issues,
• present different views and evaluate in a critical way. 

The students are able to

• familiarize in a specific topic of Computer Science in limited time,
• realize a literature survey on the specific topic and cite in a correct way,
  
• elaborate a presentation and give a lecture to a selected audience,
• sum up the presentation in 10-15 lines,
• answer questions in the final discussion.

The students are able to

• elaborate and introduce a topic for a certain audience,
• discuss the topic, content and structure of the presentation with the instructor,
• discuss certain aspects with the audience, and
• as the lecturer listen and respond to questions from the audience.

The students  

• can efficiently solve scientific problems in a modern programming language.
• are familiar with the concept of reproducible science.
• can handle multidimensional arrays, sparse arrays, data frames and missing data. They know the advantages and disadvantages of specific data structures.
• know various ways of presenting data, data relationships and error measures in a suitable way. They are familiar with known data formats for storing scientific data and can select a suitable format for specific data.

Students are able 

• to translate complex problems from a mathematical formulation into a suitable program.
• to divide a complex problem into subproblems which can be implemented modularly.
• to identify numerical standard problems and to use suitable standard algorithms which are available in libraries.
• to write maintainable program code, the correctness of which is verified by suitable tests.
• to measure the runtime of programs, to identify bottlenecks and to apply suitable acceleration techniques.

Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.

The students know

• general principles of machine learning learning: supervised/unsupervised learning, generative/descriptive learning, parametric/non-parametric learning
• different learning methods: neural networks, support vector machines, clustering, dimensionality reduction, kernel methods
• fundamentals of statistical learning theory
• advanced techniques such as transfer learning, reinforcement learning, generative adversarial networks and adaptive control

The students can

• apply machine learning methods to concrete problems
• select and evaluate suitable methods for specific problems
• evaluate the quality of a trained data-driven model
• work with known software frameworks for machine learning
• adapt the architecture and cost function of neural networks to specific problems
• show the limits of machine learning methods

Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.

The course provides basic knowledge about the structure, functionality and system-level use of operating systems. Using the model of a multi-level machine, students learn about operating system abstractions such as processes, threads, virtual memory, files, device files and inter-process communication, as well as techniques for their efficient implementation. This includes strategies for process scheduling, latency minimization through buffering, and main and background memory management. Furthermore, they know the topics of security in the operating system context and aspects of system-oriented software development in C. In the lecture-accompanying exercises, they deepened material practically on the basis programming tasks in C from the range of the UNIX system programming. The students are familiar with the operating system functions for single-processor systems. They have become familiar with special issues relating to multiprocessor systems (based on shared memory) in passing and in relation to functions for coordinating concurrent programs. Similarly, they know the topic of real-time processing to some extent only in relation to process scheduling.

Students will be able to use the POSIX system interface to access the various resources of the computing system. They are able to grasp technical documentation in order to implement complex interaction protocols. They are able to recognize concurrency problems and avoid them with blocking synchronization primitives.

Students are able to discuss and collaboratively present a problem in small groups with reference to operating systems and systems software.

This module presents advanced concepts from the discipline of computer architecture. In the beginning, a broad overview over various programming models is given, both for general-purpose computers and for special-purpose machines (e.g., signal processors). Next, foundational aspects of the micro-architecture of processors are covered. Here, the focus particularly lies on the so-called pipelining and the methods used for the acceleration of instruction execution used in this context. The students get to know concepts for dynamic scheduling, branch prediction, superscalar execution of machine instructions and for memory hierarchies.

The students are able to describe the organization of processors. They know the different architectural principles and programming models. The students examine various structures of pipelined processor architectures and are able to explain their concepts and to analyze them w.r.t. criteria like, e.g., performance or energy efficiency. They evaluate different structures of memory hierarchies, know parallel computer architectures and are able to distinguish between instruction- and data-level parallelism.

Students are able to solve similar problems alone or in a group and to present the results accordingly.

Students can 

• name the main security risks when using Information and Communication Systems, 

• name the fundamental security mechanisms,   

• name the fundamental principles of data protection.

Students can

• evaluate the strenghts and weaknesses of the fundamental security mechanisms, 

• apply the fundamental principles of data protection to concrete cases.

After successful completion of the course, students know:

• Basic concepts for data preparation
• Similarity and distance measures
• Methods to mine data patterns
• Procedures to analyse clusters
• Approaches to identify outliers 
• Data mining for different types of data, e.g., data streams, text data, time series data

Students are able to analyze large, heterogeneous volumes of data. They know methods and their application to recognize patterns in data sets and data clusters. The students are able to apply the studied methods in different domains, e.g., for data streams, text data, or time series data.

Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.

Students will learn the architecture and organization of heterogenous embedded platforms consisting of multi-core CPUs and many-core GPUs such as Jetson boards from NVIDIA. Students will program them using Python/CUDA/C++. Depending upon the specific requirements of projects, students will learn the deployment of various deep learning frameworks, such as TensorFlow, and PyTorch, on embedded platforms. In addition, students will also develop expertise in various domains such as space applications and autonomous driving. 

By the end of this module, students will have mastered the intricacies of embedded boards encompassing GPUs especially Jetson boards from NVIDIA and gained invaluable experience in real-world project development (using various deep learning frameworks) within the dynamic fields of space and autonomous driving.

By participating in team projects, students gain a holistic set of soft and social skills, including communication skills, and collaboration in teamwork, that not only contribute to their academic success but also prepare them for success in their future careers and personal interactions.

Students explain the workings of a compiler and break down a compilation task in different phases. They apply and modify the major algorithms for compiler construction and code improvement. They can re-write those algorithms in a programming language, run and test them. They choose appropriate internal languages and representations and justify their choice. They explain and modify implementations of existing compiler frameworks and experiment with frameworks and tools. 

Students design and implement arbitrary compilation phases. They integrate their code in existing compiler frameworks. They organize their compiler code properly as a software project. They generalize algorithms for compiler construction to algorithms that analyze or synthesize software. 

Students develop the software in a team. They explain problems and solutions to their team members. They present and defend their software in class. They communicate in English.

Students explain the fundamental concepts of agile methods, describe the process of 
test-driven development, and explain how continuous integration can be used in 
different scenarios. They give examples of selected pitfalls in software development, 
regarding scalability and other non-functional requirements. They write unit tests and
build scripts and combine them in a corresponding integration
environment. They explain major activities in requirements analysis,
program comprehension, and agile project development.

For a given task on a legacy system, students identify the corresponding
parts in the system and select an appropriate method for understanding the
details. They choose the proper approach of splitting a task in
independent testable and extensible pieces and, thus, solve the task
with proper methods for quality assurance. They design tests for 
legacy systems, create automated builds, and find errors at different
levels. They integrate the resulting artifacts in a continuous
development environment

Students discuss different design decisions in a group. They defend their solutions orally. They communicate in English.

Embedded systems can be defined as information processing systems embedded into enclosing products. This course teaches the foundations of such systems. In particular, it deals with an introduction into these systems (notions, common characteristics) and their specification languages (models of computation, hierarchical automata, specification of distributed systems, task graphs, specification of real-time applications, translations between different models).

Another part covers the hardware of embedded systems: Sonsors, A/D and D/A converters, real-time capable communication hardware, embedded processors, memories, energy dissipation, reconfigurable logic and actuators. The course also features an introduction into real-time operating systems, middleware and real-time scheduling. Finally, the implementation of embedded systems using hardware/software co-design (hardware/software partitioning, high-level transformations of specifications, energy-efficient realizations, compilers for embedded processors) is covered.

Students are able to solve similar problems alone or in a group and to present the results accordingly.

• Students can name the basic concepts in Mathematics IV. They are able to explain them using appropriate examples.
• Students can discuss logical connections between these concepts.  They are capable of illustrating these connections with the help of examples.
• They know proof strategies and can reproduce them.

• Students can model problems in Mathematics IV with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods.
• Students are able to discover and verify further logical connections between the concepts studied in the course.
• For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.

• Students are able to work together in teams. They are capable to use mathematics as a common language.
• In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

• Fundamentals of space electronics,
• Subcomponents of satellite systems
• Fragmentation and planning of primary missions
• Active participation in CubeSat mission to apply learned skills
• Soft skills in project management, project planning and project communication

Upon completion of the module, students will have learned fundamentals of space electronics. They also know how to plan primary missions and how to define subsystems to achieve this primary mission (requirements analysis, performance specification). They will be actively involved in missions and will be expected to put what they have learned into practice there. Additional soft skills in the area of general project management will be taught and applied through collaboration with the students.

• Basic teaching
• Conceptual design of subsystems (description of requirements and services)
• Project planning and fragmentation of primary missions (space missions)
• Practical application in CubeSat mission

The work takes place alternately in the entire group, but also in small groups. This requires close cooperation and coordination within the individual teams. The goal is for students to gain a sound knowledge of space electronics and space missions on the one hand, to apply this knowledge on the other hand and to generate sustainability of their results by working in small groups. This can be, for example, the passing on of the requirement and performance specifications, which act as a basis, starting point and result across semesters.

Students can name the basic concepts of computer-assisted geometry, describe them with mathematical precision, and explain them by means of examples.

Students are conversant with the computational description of geometrical (combinational/topological) facts, including determinant formulas and complexity assessments and proofs for all algorithms, especially output-sensitive algorithms.

Students are able to discuss logical connections between these concepts and to explain them by means of examples.

Students can model tasks from computer-assisted geometry with the aid of the concepts about which they have learnt and can solve them by means of the methods they have learnt.

Students are able to discuss with other attendees their own algorithmic suggestions for solving the problems presented. They are also able to work in teams and are conversant with mathematics as a common language.

• Students can name the basic concepts in Combinatorics and Algorithms. They are able to explain them using appropriate examples.
• Students can discuss logical connections between these concepts.  They are capable of illustrating these connections with the help of examples.
• They know proof strategies and can reproduce them.

• Students can model problems in Combinatorics and Algorithms with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods.
• Students are able to discover and verify further logical connections between the concepts studied in the course.
• For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.

• Students are able to work together in teams. They are capable to use mathematics as a common language.
• In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

Quantum computing is among the most exciting applications of quantum mechanics. Quantum algorithms can efficiently solve computational problems that have a prohibitive runtime on traditional computers. Such problems include, for instance, factoring of integer numbers or energy estimation problems from quantum chemistry and material science.

This course provides an introduction to the topic. An emphasis will be put on conceptual and mathematical aspects.

• Rigorous understanding of how quantum algorithms work and the ability to analyze them
• Connection of concepts in quantum mechanics and computer science
• Basic knowledge required to start programming a quantum computer
• Ability to solve exercises related to quantum algorithms
  

After completing this module, students are expected to be able to work on subject-specific tasks alone or in a group and to present the results appropriately. Moreover, students will be trained to identify and defuse misleading statements related to quantum computing, which can often be found in popular media.

• Students can name the basic concepts in Statistics. They are able to explain them using appropriate examples.
• Students can discuss logical connections between these concepts.  They are capable of illustrating these connections with the help of examples.
  

• Students can model statistical problems with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. They are able to use the statistical software R.
  
• Students are able to discover and verify further logical connections between the concepts studied in the course.
• For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.

• Students are able to work together (e.g. on their regular home work) in heterogeneously composed teams and to present their results appropriately (e.g. during exercise class).
• In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers. 

Students can

• list classical and modern iteration methods and their interrelationships,
• repeat convergence statements for iterative methods,
• explain aspects regarding the efficient implementation of iteration methods.
  

Students are able to

• analyse, implement, test, and compare iterative methods,
• analyse the convergence behaviour of iterative methods and, if applicable, compute congergence rates.
  

Students are able to

• work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.

Students can

• Describe input-output systems polynomially
• Explain factorization approaches to transfer functions
• Name stabilization conditions for systems in coprime stable factorization.

Students are able to

• Undertake a synthesis of stable control loops
• Apply suitable methods of analysis and synthesis to describe all stable control loops
• Ensure the fulfillment of specified performance measurements.

The students can explain principles of medical technology, including imaging systems, computer aided surgery, and medical information systems. They are able to give an overview of regulatory affairs and standards in medical technology.

The students are able to evaluate systems and medical devices in the context of clinical applications.

The students describe a problem in medical technology as a project, and define tasks that are solved in a joint effort.
The students can critically reflect on the results of other groups and make constructive suggestions for improvement.

Cyber-Physical Systems (CPS) are tightly integrated with their surrounding environment, via sensors, A/D and D/A converters, and actors. Due to their particular application areas, highly specialized sensors, processors and actors are common. Accordingly, there is a large variety of different specification approaches for CPS - in contrast to classical software engineering approaches.

Based on practical experiments using robot kits and computers, the basics of specification and modelling of CPS are taught. The lab introduces into the area (basic notions, characteristical properties) and their specification techniques (models of computation, hierarchical automata, data flow models, petri nets, imperative approaches). Since CPS frequently perform control tasks, the lab's experiments will base on simple control applications. The experiments will use state-of-the-art industrial specification tools (MATLAB/Simulink, LabVIEW, NXC) in order to model cyber-physical models that interact with the environment via sensors and actors.

Students are able to solve similar problems alone or in a group and to present the results accordingly.