The students acquire the following knowledge:

• They know basic elements of the programming language C. They know the basic data types and know how to use them.

• They have an understanding of elementary compiler tasks, of the preprocessor and programming environment and know how those interact.

• They know how to bind programs and how to include external libraries to enhance software packages.

• They know how to use header files and how to declare function interfaces to create larger programming projects.

• The acquire some knowledge how the program interacts with the operating system. This allows them to develop programs interacting with the programming environment as well.

• They learnt several possibilities how to model and implement frequently occurring standard algorithms.

• The students know how to judge the complexity of an algorithms and how to program algorithms efficiently.

• The students are able to model and implement algorithms for a number of standard functionalities. Moreover, they are able to adapt a given API.

The students acquire the following skills:

• They are able to work in small teams to solve given weekly tasks, to identify and analyze programming errors and to present their results.

• They are able to explain simple phenomena to each other directly at the PC.

• They are able to plan and to work out a project in small teams.

• They communicate final results and present programs to their tutor.

• Students can name the basic concepts in 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 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 (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).

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.

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 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 can name the basic concepts in analysis. 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 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).

Students can apply algorithms for solving decision problems, and they can justify whether approximation techniques are good enough in various application contexts, i.e., students can derive estimators and judge whether they are applicable or reliable.

- 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).

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 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 are able to

• name numerical methods for interpolation, integration, least squares problems, eigenvalue problems, nonlinear root finding problems and to explain their core ideas,
• repeat convergence statements for the numerical methods,

• explain aspects for the practical execution of numerical methods with respect to computational and storage complexitx.

  
  

Students are able to

• implement, apply and compare numerical methods using MATLAB,
• justify the convergence behaviour of numerical methods with respect to the problem and solution algorithm,
• select and execute a suitable solution approach for a given problem.
  

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 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 can explain the general architecture of an application system that is based on a database. They describe the syntax and semantics of the Entity Relationship conceptual modeling languages, and they can enumerate basic decision problems and know which features of a domain model can be captured with ER and which features cannot be represented. Furthermore, students can summarize the features of the relational data model, and can describe how ER models can be systematically transformed into the relational data model. Student are able to discuss dependency theory using the operators of relational algebra, and they know how to use relational algebra as a query language. In addition, they can sketch the main modules of the architecture of a database system from an implementation point of view. Storage and index structures as well as query answering and optimization techniques can be explained. The role of transactions can be described in terms of ACID conditions and common recovery mechanisms can be characterized. The students can recall why recursion is important for query languages and describe how Datalog can be used and implemented.They demonstrate how Datalog can be used for information integration. For solving ER decision problems the students can explain description logics with their syntax and semantics, they describe description logic decision problems and explain how these problems can be mapped onto each other. They can sketch the idea of ontology-based data access and can name the main complexity measure in database theory. Last but not least, the students can describe the main features of XML and can explain XPath and XQuery as query languages.

Students can apply ER for describing domains for which they receive a textual description, and students can transform relational schemata with a given set of functional dependencies into third normal form or even Boyce-Codd normal form. They can also apply relational algebra, SQL, or Datalog to specify queries. Using specific datasets, they can explain how index structures work (e.g., B-trees) and how index structures change while data is added or deleted. They can rewrite queries for better performance of query evaluation. Students can analyse which query language expressivity is required for which application problem. Description logics can be applied for domain modeling, and students can transform ER diagrams into description logics in order to check for consistency and implicit subsumption relations.  They solve data integration problems using Datalog and LAV or GAV rules. Students can apply XPath and Xquery to retrieve certain patterns in XML data.

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.

• 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.

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  

• 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.

Students can 

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

• describe commonly used methods for risk and security analysis,  

• name the fundamental principles of data protection.

Students can

• evaluate the strenghts and weaknesses of the fundamental security mechanisms and of the commonly used methods for risk and security analysis, 

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

Students get to know tools used by development teams to

• plan development flows,
• mine, process and analyze data
  
• train and validate data-orientated models
• follow good practice in software engineering

Students work in teams on a larger data project. The required competences are learned and practically applied. These are for example:

• project specification based on user requirements
• creating a data-orientated software architecture
• mining, preprocessing and analyzing larger datasets
• implementing a learning platform in a team
• comparison of different learning methods
• performing statistical tests

Team work has its own challenges with respect to interaction of team members as well as finding the necessary agreement during joint software development. During the project students learn the required competences and experience the practical needs.

The students know

• various forms of knowledge representation
• different methods for cluster analysis and classification
• methods for data preprocessing
• tools for the visualization of large amounts of data
• methods for the evaluation of data
• text, web and stream mining; time series analysis
  
  

Students are able to analyze large, heterogeneous data using clustering and classification methods. They can preprocess the data so that a data-driven model  can be trained with homogeneous data. The students are able to visualize large amounts of data and their internal structures and to evaluate the 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.

The students know about

• visual perception
• multidimensional signal processing
• sampling and sampling theorem
• filtering
• image enhancement
• edge detection
• multi-resolution procedures: Gauss and Laplace pyramid, wavelets
• image compression
• image segmentation
• morphological image processing

The students can

• analyze, process, and improve multidimensional image data
• implement simple compression algorithms
• design custom filters for specific applications

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 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.

• 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.

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.

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.

Students are able to give an overview of conventional and modern electric power systems.  They can explain in detail and critically evaluate technologies of electric power generation, transmission, storage, and distribution as well as integration of equipment into electric power systems.

With completion of this module the students are able to apply the acquired skills in applications of the design, integration, development of electric power systems and to assess the results.

The students can participate in specialized and interdisciplinary discussions, advance ideas and represent their own work results in front of others.

 The students can jointly solve specific problems.

The students are able to explain the purpose of metrology and the acquisition and processing of measurements. They can detail aspects of probability theory and errors, and explain the processing of stochastic signals. Students know methods to digitalize and describe measured signals. Data processing from acquisition to regression and classification can be described in context.

The students are able to evaluate problems of metrology and to apply methods for describing and processing of measurements.

The students solve problems in small groups. An actual problem including data acquisition and data processing is solved in groups.

The students can

• describe the axiomatic procedure used in mechanical contexts;
• explain important steps in model design;
• present technical knowledge in stereostatics.

The students can

• explain the important elements of mathematical / mechanical analysis and model formation, and apply it to the context of their own problems;
• apply basic hydrostatical, kinematic and kinetic methods to engineering problems;
• estimate the reach and boundaries of statical methods and extend them to be applicable to wider problem sets.

The students can work in groups and support each other to overcome difficulties.

Students can…

• Explain the structure and workings of standard external logistics systems.
• Outline the benefits of using simulation software subject to the starting situation.
• Present different simulation programs and kinds of simulation that are in widespread use and explain their characteristics.

Students are able to…

• Recognize, analyze, and assemble into a model the elementary building blocks of a logistics system.
• Map complex external logistics process using the Plant Simulation® simulation software.
• Draw inferences from the results of the simulation, transfer them to the reality, and deduce action recommendations from them.

Students are capable of…

• Solving complex tasks in a team and to document assignments accordingly.
• Playing different roles in the teamwork and giving each other appropriate feedback in the team.
• Presenting the relevant results of their project to specialists and representing them.

• Students can represent dynamic system behavior in time and frequency domain, and can in particular explain properties of first and second order systems
• They can explain the dynamics of simple control loops and interpret dynamic properties in terms of frequency response and root locus
• They can explain the Nyquist stability criterion and the stability margins derived from it.
• They can explain the role of the phase margin in analysis and synthesis of control loops
• They can explain the way a PID controller affects a control loop in terms of its frequency response
• They can explain issues arising when controllers designed in continuous time domain are implemented digitally

• Students can transform models of linear dynamic systems from time to frequency domain and vice versa
• They can simulate and assess the behavior of systems and control loops
• They can design PID controllers with the help of heuristic (Ziegler-Nichols) tuning rules
• They can analyze and synthesize simple control loops with the help of root locus and frequency response techniques
• They can calculate discrete-time approximations of controllers designed in continuous-time and use it for digital implementation
• They can use standard software tools (Matlab Control Toolbox, Simulink) for carrying out these tasks

Students can

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

Students are able to

• 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.

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.

• Students are able to explain the functionality of different MOS devices in electronic circuits.
• Students are able to explain how analog circuits functions and where they are applied.
• Students are able to explain the functionality of fundamental operational amplifiers and their specifications.
• Students know the fundamental digital logic circuits and can discuss their advantages and disadvantages.
• Students have knowledge about memory circuits and can explain their functionality and specifications.
  
• Students know the appropriate fields for the use of bipolar transistors.

• Students can calculate the specifications of different MOS devices and can define the parameters of electronic circuits.
• Students are able to develop different logic circuits and can design different types of logic circuits.
  
• Students can use MOS devices, operational amplifiers and bipolar transistors for specific applications.

• Students are able work efficiently in heterogeneous teams.
• Students working together in small groups can solve problems and answer professional  questions.

The students known the important machine models of computability, the class of partial recursive functions, universal computability, Gödel numbering of computations, the theorems of Kleene, Rice, and Rice-Shapiro, the concept of decidable and undecidable sets, the word problems for semi-Thue systems, Thue systems, semi-groups, and Post correspondence systems, Hilbert's 10-th problem, and the basic concepts of complexity theory.

Students are able to investigate the computability of sets and functions and to analyze the complexity of computable functions.

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

The students are able to give an enhanced overview over the following topics
in metals, polymers and ceramics: Atomic bonds, crystal and amorphous structures, defects , electrical and mass transport, microstructure and phase diagrams. They are capable to explain the corresponding technical terms.

The students are able to apply the appropriate physical and chemical methods for  the above mentioned subjects.