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 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.

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 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 reproduce and explain fundamental theories, principles, and methods related to the theory of alternating currents. They can describe networks of linear elements using a complex notation for voltages and currents. They can reproduce an overview of applications for the theory of alternating currents in the area of electrical engineering. Students are capable of explaining the behavior of fundamental passive and active devices as well as their impact on simple circuits.

Students are capable of calculating parameters within simple electrical networks at alternating currents by means of a complex notation for voltages and currents. They can appraise the fundamental effects that may occur within electrical networks at alternating currents. Students are able to analyze simple circuits such as oscillating circuits, filter, and matching networks quantitatively and dimension elements by means of a design. They can motivate and justify the fundamental elements of an electrical power supply (transformer, transmission line, compensation of reactive power, multiphase system) and are qualified to dimension their main features.

Students are able to work together on subject related tasks in small groups. They are able to present their results effectively (e.g. during a week of project work).

Students can explain the essentials of software design and the design of a class architecture with reference to existing class libraries and design patterns.

Students can describe fundamental data structures of discrete mathematics and assess the complexity of important algorithms for sorting and searching.

Students are able to

• Design software using given design patterns and applying class hierarchies and polymorphism
• Carry out software development and tests using version management systems and Google Test
• Sort and search for data efficiently
• Assess the complexity of algorithms.

Students can work in teams and communicate in forums.

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.

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. 
  

• Students can name further 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 work goal-oriented in small mixed groups, learning and broadening teamwork abilities.

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

• 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.

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 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 work goal-oriented in small mixed groups, learning and broadening teamwork abilities.

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 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 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 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).

Students explain the main abstractions process, virtual memory, deadlock, lifelock, and file of operations systems, describe the process states and their transitions, and paraphrase the architectural variants of operating systems. They give examples of existing operating systems and explain their architectures. The participants of the course write concurrent programs using threads, conditional variables and semaphores. Students can describe the variants of realizing a file system. Students explain at least three different scheduling algorithms.

Students are able to use the POSIX libraries for concurrent programming in a correct and efficient way. They are able to judge the efficiency of a scheduling algorithm for a given scheduling task in a given environment.

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 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.

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 know the difference between precision and accuracy. For several basic problems they know how to solve them approximatively and exactly. They can distinguish between efficiently, not efficiently and principally unsolvable problems.

The students are able to analyze complex problems in mathematics and computer science. In particular they can analyze the sensitivity of the solution. For several problems they can derive best possible algorithms with respect to the accuracy of the computed result.

The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner.

Students explain the main abstractions of Distributed Systems (Marshalling, proxy, service, address, Remote procedure call, synchron/asynchron system). They describe the pros and cons of different types of interprocess communication. They give examples of existing middleware solutions. The participants of the course know the main architectural variants of distributed systems, including their pros and cons. Students can describe at least three different synchronization mechanisms.

Students can realize distributed systems using at least three different techniques:

• Proprietary protocol realized with TCP
• HTTP as a remote procedure call
• RMI as a middleware
  

• 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.

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.

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.

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.

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 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.

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.

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.

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 can describe basic concepts in Mathematical Statistics such as the substitution and Maximum-Likelihood methods for construction of estimators, optimal unfalsified estimators, optimal tests for parametric probability distributions,  sufficiency and completeness and their application to estimation and test problems, tests in normal distribution and confidence domains and test families. 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 Mathematical Statistics 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.