Program description
Content
Core Qualification
Module M0561: Discrete Algebraic Structures |
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Courses | ||||||||||||
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Module Responsible | Prof. Karl-Heinz Zimmermann |
Admission Requirements | None |
Recommended Previous Knowledge |
Mathematics from High School. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
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. |
Skills |
Students are able to formalize and analyze basic discrete algebraic structures. |
Personal Competence | |
Social Competence |
Students are able to solve specific problems alone or in a group and to present the results accordingly. |
Autonomy |
Students are able to acquire new knowledge from specific standard books and to associate the acquired knowledge to other classes. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 120 min |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Orientation Studies: Core Qualification: Elective Compulsory |
Course L0164: Discrete Algebraic Structures |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Karl-Heinz Zimmermann |
Language | DE/EN |
Cycle | WiSe |
Content | |
Literature |
Course L0165: Discrete Algebraic Structures |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Karl-Heinz Zimmermann |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0731: Functional Programming |
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Courses | ||||||||||||||||
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Module Responsible | Prof. Sibylle Schupp | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge | Discrete mathematics at high-school level | ||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
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. |
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Skills |
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. |
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Personal Competence | |||||||||
Social Competence |
Students practice peer programming with varying peers. They explain problems and solutions to their peer. They defend their programs orally. They communicate in English. |
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Autonomy |
In programming labs, students learn under supervision (a.k.a. "Betreutes Programmieren") the mechanics of programming. In exercises, they develop solutions individually and independently, and receive feedback. |
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Workload in Hours | Independent Study Time 96, Study Time in Lecture 84 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
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Examination | Written exam | ||||||||
Examination duration and scale | 90 min | ||||||||
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L0624: Functional Programming |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
Graham Hutton, Programming in Haskell, Cambridge University Press 2007. |
Course L0625: Functional Programming |
Typ | Recitation Section (large) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
Graham Hutton, Programming in Haskell, Cambridge University Press 2007. |
Course L0626: Functional Programming |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
Graham Hutton, Programming in Haskell, Cambridge University Press 2007. |
Module M1436: Procedural Programming for Computer Engineers |
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Courses | ||||||||||||||||
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Module Responsible | Prof. Bernd-Christian Renner |
Admission Requirements | None |
Recommended Previous Knowledge | None |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students will know
|
Skills |
- Mastery of typical development tools |
Personal Competence | |
Social Competence |
- After completing the module, students are able to work on subject-specific tasks alone or in a group and to present the results appropriately. |
Autonomy |
- After completion of the module, students
are able to work independently on parts of the subject area using
reference books, to summarize the acquired knowledge, |
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 120 min |
Assignment for the Following Curricula |
Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Orientation Studies: Core Qualification: Elective Compulsory Technomathematics: Core Qualification: Compulsory |
Course L2163: Procedural Programming for Computer Engineers |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Bernd-Christian Renner |
Language | DE/EN |
Cycle | WiSe |
Content |
- Development tools: preprocessor, compiler, linker, assembler, IDE, version management (Git) |
Literature |
- Greg Perry and Dean Miller. C Programming Absolute Beginner's Guide: No experience necessary! Que Publishing; 3. Auflage (7. August 2013). ISBN 978-0789751980. |
Course L2164: Procedural Programming for Computer Engineers |
Typ | Recitation Section (large) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Bernd-Christian Renner |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L2165: Procedural Programming for Computer Engineers |
Typ | Practical Course |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Bernd-Christian Renner |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1728: Mathematics I (EN) |
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Courses | ||||||||||||||||
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Module Responsible | Prof. Daniel Ruprecht | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
School mathematics |
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Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
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Skills |
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Personal Competence | |||||||||
Social Competence |
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Autonomy |
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Workload in Hours | Independent Study Time 128, Study Time in Lecture 112 | ||||||||
Credit points | 8 | ||||||||
Course achievement |
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Examination | Written exam | ||||||||
Examination duration and scale | 120 min | ||||||||
Assignment for the Following Curricula |
Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory |
Course L2973: Mathematics I (EN) |
Typ | Lecture |
Hrs/wk | 4 |
CP | 4 |
Workload in Hours | Independent Study Time 64, Study Time in Lecture 56 |
Lecturer | Prof. Anusch Taraz |
Language | EN |
Cycle | WiSe |
Content |
Mathematical Foundations: sets, statements, induction, mappings, trigonometry Analysis: Foundations of differential calculus in one variable
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Literature |
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Course L2974: Mathematics I (EN) |
Typ | Recitation Section (large) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Anusch Taraz, Dr. Dennis Clemens, Dr. Simon Campese |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L2975: Mathematics I (EN) |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Anusch Taraz |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1755: Linking theory and practice (dual study program, Bachelor's degree) |
Module Responsible | Dr. Henning Haschke |
Admission Requirements | None |
Recommended Previous Knowledge | none |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Dual students… … can describe and classify selected classic and modern theories, concepts and methods
... and apply them to specific situations, projects and plans in a personal and professional context. |
Skills |
Dual students…
|
Personal Competence | |
Social Competence |
Dual students…
|
Autonomy |
Dual students…
|
Workload in Hours | Independent Study Time 96, Study Time in Lecture 84 |
Credit points | 6 |
Course achievement | None |
Examination | Written elaboration |
Examination duration and scale | Studienbegleitende und semesterübergreifende Dokumentation: Die Leistungspunkte für das Modul werden durch die Anfertigung eines digitalen Lern- und Entwicklungsberichtes (E-Portfolio) erworben. Dabei handelt es sich um eine fortlaufende Dokumentation und Reflexion der Lernerfahrungen und der Kompetenzentwicklung im Bereich der Personalen Kompetenz. |
Course L2885: Self-Competence for Professional Success in Engineering (for Dual Study Program) |
Typ | Seminar |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Dr. Henning Haschke, Heiko Sieben |
Language | DE |
Cycle |
WiSe/ |
Content |
|
Literature | Seminarapparat |
Course L2884: Self-Management, Organising Work and Learning in Engineering (for Dual Study Program) |
Typ | Seminar |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Dr. Henning Haschke, Heiko Sieben |
Language | DE |
Cycle |
WiSe/ |
Content |
|
Literature | Seminarapparat |
Course L2886: Social-Competence: Team Development and Communication in Engineering (for Dual Study Program) |
Typ | Seminar |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Dr. Henning Haschke, Heiko Sieben |
Language | DE |
Cycle |
WiSe/ |
Content |
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Literature | Seminarapparat |
Module M1750: Practical module 1 (dual study program, Bachelor's degree) |
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Courses | ||||||||
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Module Responsible | Dr. Henning Haschke |
Admission Requirements | None |
Recommended Previous Knowledge |
A: Self-management, organising work and learning in engineering (for dual study program) |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Dual students…
|
Skills |
Dual students…
|
Personal Competence | |
Social Competence |
Dual students…
|
Autonomy |
Dual students…
|
Workload in Hours | Independent Study Time 180, Study Time in Lecture 0 |
Credit points | 6 |
Course achievement | None |
Examination | Written elaboration |
Examination duration and scale | Documentation accompanying studies and across semesters: Module credit points are earned by completing a digital learning and development report (e-portfolio). This documents and reflects individual learning experiences and skills development relating to interlinking theory and practice, as well as professional practice. In addition, the partner company provides proof to the dual@TUHH Coordination Office that the dual student has completed the practical phase. |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory |
Course L2879: Practical term 1 (dual study program, Bachelor's degree) |
Typ | |
Hrs/wk | 0 |
CP | 6 |
Workload in Hours | Independent Study Time 180, Study Time in Lecture 0 |
Lecturer | Dr. Henning Haschke |
Language | DE |
Cycle | WiSe |
Content |
Company onboarding process
Operational knowledge and skills
Sharing/reflecting on learning
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Literature |
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Module M0624: Automata Theory and Formal Languages |
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Courses | ||||||||||||
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Module Responsible | Prof. Matthias Mnich |
Admission Requirements | None |
Recommended Previous Knowledge |
Participating students should be able to - specify algorithms for simple data structures (such as, e.g., arrays) to solve computational problems - apply propositional logic and predicate logic for specifying and understanding mathematical proofs - apply the knowledge and skills taught in the module Discrete Algebraic Structures |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
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. |
Skills |
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. |
Personal Competence | |
Social Competence |
|
Autonomy |
|
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Elective Compulsory Computer Science in Engineering: Core Qualification: Compulsory Orientation Studies: Core Qualification: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L0332: Automata Theory and Formal Languages |
Typ | Lecture |
Hrs/wk | 2 |
CP | 4 |
Workload in Hours | Independent Study Time 92, Study Time in Lecture 28 |
Lecturer | Prof. Matthias Mnich |
Language | EN |
Cycle | SoSe |
Content |
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Literature |
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Course L0507: Automata Theory and Formal Languages |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Matthias Mnich |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0829: Foundations of Management |
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Courses | ||||||||||||
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Module Responsible | Prof. Christoph Ihl |
Admission Requirements | None |
Recommended Previous Knowledge | Basic Knowledge of Mathematics and Business |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
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
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Skills |
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
|
Personal Competence | |
Social Competence |
Students are able to
|
Autonomy |
Students are able to
|
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Credit points | 6 |
Course achievement | None |
Examination | Subject theoretical and practical work |
Examination duration and scale | several written exams during the semester |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Specialisation Civil Engineering: Elective Compulsory Civil- and Environmental Engineering: Specialisation Water and Environment: Elective Compulsory Civil- and Environmental Engineering: Specialisation Traffic and Mobility: Elective Compulsory Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Integrated Building Technology: Core Qualification: Compulsory Logistics and Mobility: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Orientation Studies: Core Qualification: Elective Compulsory Orientation Studies: Core Qualification: Elective Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Core Qualification: Compulsory Process Engineering: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory |
Course L0882: Management Tutorial |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Christoph Ihl, Katharina Roedelius |
Language | DE |
Cycle |
WiSe/ |
Content |
In the management tutorial, the contents of the lecture will be deepened by practical examples and the application of the discussed tools. If there is adequate demand, a problem-oriented tutorial will be offered in parallel, which students can choose alternatively. Here, students work in groups on self-selected projects that focus on the elaboration of an innovative business idea from the point of view of an established company or a startup. Again, the business knowledge from the lecture should come to practical use. The group projects are guided by a mentor. |
Literature | Relevante Literatur aus der korrespondierenden Vorlesung. |
Course L0880: Introduction to Management |
Typ | Lecture |
Hrs/wk | 3 |
CP | 3 |
Workload in Hours | Independent Study Time 48, Study Time in Lecture 42 |
Lecturer | Prof. Christoph Ihl, Prof. Christian Lüthje, Prof. Christian Ringle, Prof. Cornelius Herstatt, Prof. Kathrin Fischer, Prof. Matthias Meyer, Prof. Thomas Wrona, Prof. Thorsten Blecker, Prof. Wolfgang Kersten |
Language | DE |
Cycle |
WiSe/ |
Content |
|
Literature |
Bamberg, G., Coenenberg, A.: Betriebswirtschaftliche Entscheidungslehre, 14. Aufl., München 2008 Eisenführ, F., Weber, M.: Rationales Entscheiden, 4. Aufl., Berlin et al. 2003 Heinhold, M.: Buchführung in Fallbeispielen, 10. Aufl., Stuttgart 2006. Kruschwitz, L.: Finanzmathematik. 3. Auflage, München 2001. Pellens, B., Fülbier, R. U., Gassen, J., Sellhorn, T.: Internationale Rechnungslegung, 7. Aufl., Stuttgart 2008. Schweitzer, M.: Planung und Steuerung, in: Bea/Friedl/Schweitzer: Allgemeine Betriebswirtschaftslehre, Bd. 2: Führung, 9. Aufl., Stuttgart 2005. Weber, J., Schäffer, U. : Einführung in das Controlling, 12. Auflage, Stuttgart 2008. Weber, J./Weißenberger, B.: Einführung in das Rechnungswesen, 7. Auflage, Stuttgart 2006. |
Module M1432: Programming Paradigms |
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Courses | ||||||||||||||||
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Module Responsible | NN |
Admission Requirements | None |
Recommended Previous Knowledge |
Lecture on procedural programming or equivalent programming skills |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
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. |
Skills |
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. |
Personal Competence | |
Social Competence |
Students can work in teams and communicate in forums. |
Autonomy |
In a programming internship, students learn object-oriented programming under supervision. In exercises they develop individual and independent solutions and receive feedback. |
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Orientation Studies: Core Qualification: Elective Compulsory Technomathematics: Core Qualification: Compulsory |
Course L2169: Programming Paradigms |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Dozenten des SD E |
Language | DE/EN |
Cycle | SoSe |
Content |
|
Literature | Skript |
Course L2170: Programming Paradigms |
Typ | Recitation Section (large) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Dozenten des SD E |
Language | DE/EN |
Cycle | SoSe |
Content |
|
Literature | Skript |
Course L2171: Programming Paradigms |
Typ | Practical Course |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Dozenten des SD E |
Language | DE/EN |
Cycle | SoSe |
Content |
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Literature | Skript |
Module M1729: Mathematics II (EN) |
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Courses | ||||||||||||||||
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Module Responsible | Prof. Daniel Ruprecht | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
School mathematics |
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Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
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Skills |
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Personal Competence | |||||||||
Social Competence |
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Autonomy |
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Workload in Hours | Independent Study Time 128, Study Time in Lecture 112 | ||||||||
Credit points | 8 | ||||||||
Course achievement |
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Examination | Written exam | ||||||||
Examination duration and scale | 120 min | ||||||||
Assignment for the Following Curricula |
Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory |
Course L2979: Mathematics II (EN) |
Typ | Lecture |
Hrs/wk | 4 |
CP | 4 |
Workload in Hours | Independent Study Time 64, Study Time in Lecture 56 |
Lecturer | Prof. Anusch Taraz |
Language | EN |
Cycle | SoSe |
Content | |
Literature |
Course L2980: Mathematics II (EN) |
Typ | Recitation Section (large) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Anusch Taraz |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L2981: Mathematics II (EN) |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Anusch Taraz |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1751: Practical module 2 (dual study program, Bachelor's degree) |
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Courses | ||||||||
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Module Responsible | Dr. Henning Haschke |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Dual students …
|
Skills |
Dual students …
|
Personal Competence | |
Social Competence |
Dual students …
|
Autonomy |
Dual students …
|
Workload in Hours | Independent Study Time 180, Study Time in Lecture 0 |
Credit points | 6 |
Course achievement | None |
Examination | Written elaboration |
Examination duration and scale | Documentation accompanying studies and across semesters: Module credit points are earned by completing a digital learning and development report (e-portfolio). This documents and reflects individual learning experiences and skills development relating to interlinking theory and practice, as well as professional practice. In addition, the partner company provides proof to the dual@TUHH Coordination Office that the dual student has completed the practical phase. |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory |
Course L2880: Practical term 2 (dual study program, Bachelor's degree) |
Typ | |
Hrs/wk | 0 |
CP | 6 |
Workload in Hours | Independent Study Time 180, Study Time in Lecture 0 |
Lecturer | Dr. Henning Haschke |
Language | DE |
Cycle | SoSe |
Content |
Company onboarding process
Operational knowledge and skills
Sharing/reflecting on learning
|
Literature |
|
Module M0834: Computernetworks and Internet Security |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Andreas Timm-Giel |
Admission Requirements | None |
Recommended Previous Knowledge |
Basics of Computer Science |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
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. |
Skills |
Students are able to analyse common Internet protocols and evaluate the use of them in different domains. |
Personal Competence | |
Social Competence |
|
Autonomy |
Students can select relevant parts out of high amount of professional knowledge and can independently learn and understand it. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 120 min |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory Computer Science: Core Qualification: Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Data Science: Core Qualification: Elective Compulsory Electrical Engineering: Core Qualification: Elective Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory Engineering Science: Specialisation Electrical Engineering: Elective Compulsory General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Elective Compulsory Computer Science in Engineering: Core Qualification: Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L1098: Computer Networks and Internet Security |
Typ | Lecture |
Hrs/wk | 3 |
CP | 5 |
Workload in Hours | Independent Study Time 108, Study Time in Lecture 42 |
Lecturer | Dr. Koojana Kuladinithi, Prof. Sibylle Fröschle |
Language | EN |
Cycle | WiSe |
Content |
In this class an introduction to computer networks with focus on the Internet and its security is given. Basic functionality of complex protocols are introduced. Students learn to understand these and identify common principles. In the exercises these basic principles and an introduction to performance modelling are addressed using computing tasks and physical labs. In the second part of the lecture an introduction to Internet security is given. This class comprises:
|
Literature |
Further literature is announced at the beginning of the lecture. |
Course L1099: Computer Networks and Internet Security |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Dr. Koojana Kuladinithi, Prof. Sibylle Fröschle |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0625: Databases |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Stefan Schulte |
Admission Requirements | None |
Recommended Previous Knowledge |
Students should have basic knowledge in the following areas:
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
After successful completion of the course, students know:
|
Skills |
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. |
Personal Competence | |
Social Competence |
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. |
Autonomy |
Students are able to independently investigate a complex problem and assess which competencies are required to solve it. |
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Data Science: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Data Science: Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L0337: Databases |
Typ | Lecture |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Stefan Schulte |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
|
Course L1150: Databases - Exercise |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Stefan Schulte |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
|
Module M0730: Computer Engineering |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Heiko Falk | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
Basic knowledge in electrical engineering |
||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
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:
|
||||||||
Skills |
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. |
||||||||
Personal Competence | |||||||||
Social Competence |
Students are able to solve similar problems alone or in a group and to present the results accordingly. |
||||||||
Autonomy |
Students are able to acquire new knowledge from specific literature and to associate this knowledge with other classes. |
||||||||
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 90 minutes, contents of course and labs | ||||||||
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory General Engineering Science (German program, 7 semester): Specialisation Electrical Engineering: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Electrical Engineering: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Integrated Building Technology: Core Qualification: Elective Compulsory Mechatronics: Core Qualification: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L0321: Computer Engineering |
Typ | Lecture |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Heiko Falk |
Language | DE/EN |
Cycle | WiSe |
Content |
|
Literature |
|
Course L0324: Computer Engineering |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Heiko Falk |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1732: Mathematics III (EN) |
||||||||||||||||||||||||||||
Courses | ||||||||||||||||||||||||||||
|
Module Responsible | Prof. Marko Lindner |
Admission Requirements | None |
Recommended Previous Knowledge |
Mathematik I and II (EN or DE) |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
|
Skills |
|
Personal Competence | |
Social Competence |
|
Autonomy |
|
Workload in Hours | Independent Study Time 128, Study Time in Lecture 112 |
Credit points | 8 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 120 min |
Assignment for the Following Curricula |
Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory |
Course L2790: Analysis III (EN) |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Dozenten des Fachbereiches Mathematik der UHH |
Language | EN |
Cycle | WiSe |
Content |
Main features of differential and integrational calculus of several variables
|
Literature |
http://www.math.uni-hamburg.de/teaching/export/tuhh/index.html |
Course L2791: Analysis III (EN) |
Typ | Recitation Section (large) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Dozenten des Fachbereiches Mathematik der UHH |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L2792: Analysis III (EN) |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Dozenten des Fachbereiches Mathematik der UHH |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L2793: Differential Equations 1 (Ordinary Differential Equations) (EN) |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Dozenten des Fachbereiches Mathematik der UHH |
Language | EN |
Cycle | WiSe |
Content |
Main features of the theory and numerical treatment of ordinary differential equations
|
Literature |
|
Course L2794: Differential Equations 1 (Ordinary Differential Equations) (EN) |
Typ | Recitation Section (large) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Dozenten des Fachbereiches Mathematik der UHH |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L2795: Differential Equations 1 (Ordinary Differential Equations) (EN) |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Dozenten des Fachbereiches Mathematik der UHH |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1423: Algorithms and Data Structures |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Matthias Mnich | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
|
||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
|
||||||||
Skills |
|
||||||||
Personal Competence | |||||||||
Social Competence |
|
||||||||
Autonomy |
|
||||||||
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 90 min | ||||||||
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory General Engineering Science (German program, 7 semester): Specialisation Data Science: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Data Science: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Logistics and Mobility: Specialisation Information Technology: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation Information Technology: Elective Compulsory |
Course L2046: Algorithms and Data Structures |
Typ | Lecture |
Hrs/wk | 4 |
CP | 4 |
Workload in Hours | Independent Study Time 64, Study Time in Lecture 56 |
Lecturer | Prof. Matthias Mnich |
Language | DE/EN |
Cycle | WiSe |
Content |
|
Literature |
|
Course L2047: Algorithms and Data Structures |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Matthias Mnich |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1752: Practical module 3 (dual study program, Bachelor's degree) |
||||||||
Courses | ||||||||
|
Module Responsible | Dr. Henning Haschke |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Dual students …
|
Skills |
Dual students …
|
Personal Competence | |
Social Competence |
Dual students …
|
Autonomy |
Dual students …
|
Workload in Hours | Independent Study Time 180, Study Time in Lecture 0 |
Credit points | 6 |
Course achievement | None |
Examination | Written elaboration |
Examination duration and scale | Documentation accompanying studies and across semesters: Module credit points are earned by completing a digital learning and development report (e-portfolio). This documents and reflects individual learning experiences and skills development relating to interlinking theory and practice, as well as professional practice. In addition, the partner company provides proof to the dual@TUHH Coordination Office that the dual student has completed the practical phase. |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory |
Course L2881: Practical term 3 (dual study program, Bachelor's degree) |
Typ | |
Hrs/wk | 0 |
CP | 6 |
Workload in Hours | Independent Study Time 180, Study Time in Lecture 0 |
Lecturer | Dr. Henning Haschke |
Language | DE |
Cycle | WiSe |
Content |
Company onboarding process
Operational knowledge and skills
Sharing/reflecting on learning
|
Literature |
|
Module M0732: Software Engineering |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Sibylle Schupp | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
|
||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
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. |
||||||||
Skills |
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. |
||||||||
Personal Competence | |||||||||
Social Competence |
Students practice peer programming. They explain problems and solutions to their peer. They communicate in English. |
||||||||
Autonomy |
Using on-line quizzes and accompanying material for self study, students can assess their level of knowledge continuously and adjust it appropriately. Working on exercise problems, they receive additional feedback. |
||||||||
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 90 min | ||||||||
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory Computer Science: Core Qualification: Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L0627: Software Engineering |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
Ian Sommerville, Engineering Software Products: An Introduction to Modern Software Engineering, Pearson 2020. Kassem A. Saleh, Software Engineering, J. Ross Publishing 2009. |
Course L0628: Software Engineering |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0562: Computability and Complexity Theory |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Martin Kliesch | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge | Discrete Algebraic Structures, Automata Theory, Logic, and Formal Language Theory | ||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
|
||||||||
Skills |
After completing this module, students are able to
|
||||||||
Personal Competence | |||||||||
Social Competence |
After completing this module, students are able to work on subject-specific tasks alone or in a group and to present the results appropriately. |
||||||||
Autonomy |
After completion of this module, students are able to work out sub-areas of the subject area independently on the basis of textbooks and other literature, to summarize and present the acquired knowledge and to link it to the contents of other courses. |
||||||||
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 90 min | ||||||||
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Data Science: Elective Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L0166: Computability and Complexity Theory |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Martin Kliesch |
Language | DE/EN |
Cycle | SoSe |
Content | |
Literature |
Course L0167: Computability and Complexity Theory |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Martin Kliesch |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0727: Stochastics |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Matthias Schulte |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
|
Skills |
|
Personal Competence | |
Social Competence |
|
Autonomy |
|
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 120 min |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory General Engineering Science (German program, 7 semester): Specialisation Advanced Materials: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Data Science: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Advanced Materials: Elective Compulsory Engineering Science: Specialisation Data Science: Compulsory Engineering Science: Specialisation Electrical Engineering: Elective Compulsory Engineering Science: Specialisation Electrical Engineering: Elective Compulsory Computer Science in Engineering: Core Qualification: Compulsory Logistics and Mobility: Specialisation Information Technology: Elective Compulsory Orientation Studies: Core Qualification: Elective Compulsory Theoretical Mechanical Engineering: Core Qualification: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation Information Technology: Elective Compulsory |
Course L0777: Stochastics |
Typ | Lecture |
Hrs/wk | 2 |
CP | 4 |
Workload in Hours | Independent Study Time 92, Study Time in Lecture 28 |
Lecturer | Prof. Matthias Schulte |
Language | DE/EN |
Cycle | SoSe |
Content |
|
Literature |
|
Course L0778: Stochastics |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Matthias Schulte |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0852: Graph Theory and Optimization |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Anusch Taraz |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
|
Skills |
|
Personal Competence | |
Social Competence |
|
Autonomy |
|
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 120 min |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory General Engineering Science (German program, 7 semester): Specialisation Data Science: Elective Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Data Science: Elective Compulsory Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory Logistics and Mobility: Specialisation Information Technology: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation Information Technology: Elective Compulsory |
Course L1046: Graph Theory and Optimization |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Anusch Taraz |
Language | DE/EN |
Cycle | SoSe |
Content |
|
Literature |
|
Course L1047: Graph Theory and Optimization |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Anusch Taraz |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1753: Practical module 4 (dual study program, Bachelor's degree) |
||||||||
Courses | ||||||||
|
Module Responsible | Dr. Henning Haschke |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Dual students …
|
Skills |
Dual students …
|
Personal Competence | |
Social Competence |
Dual students …
|
Autonomy |
Dual students …
|
Workload in Hours | Independent Study Time 180, Study Time in Lecture 0 |
Credit points | 6 |
Course achievement | None |
Examination | Written elaboration |
Examination duration and scale | Documentation accompanying studies and across semesters: Module credit points are earned by completing a digital learning and development report (e-portfolio). This documents and reflects individual learning experiences and skills development relating to interlinking theory and practice, as well as professional practice. In addition, the partner company provides proof to the dual@TUHH Coordination Office that the dual student has completed the practical phase. |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory |
Course L2882: Practical term 4 (dual study program, Bachelor's degree) |
Typ | |
Hrs/wk | 0 |
CP | 6 |
Workload in Hours | Independent Study Time 180, Study Time in Lecture 0 |
Lecturer | Dr. Henning Haschke |
Language | DE |
Cycle | SoSe |
Content |
Company onboarding process
Operational knowledge and skills
Sharing/reflecting on learning
|
Literature |
|
Module M0873: Software Industrial Internship |
||||
Courses | ||||
|
Module Responsible | Dozenten des SD E |
Admission Requirements | None |
Recommended Previous Knowledge |
Foundations
of Software Engineering |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students know the important aspects and phases of
software development. |
Skills |
Students can describe the typical phases of software development and are able to contribute to a software project. |
Personal Competence | |
Social Competence |
Students are able to specify, implement, and analyze specific basic topics in software development and present them accordingly. |
Autonomy |
Students are able to acquire new knowledge from specific literature and to associate this knowledge with other classes. |
Workload in Hours | Independent Study Time 180, Study Time in Lecture 0 |
Credit points | 6 |
Course achievement | None |
Examination | Written elaboration (accord. to Internship Regulations) |
Examination duration and scale | Die Ausarbeitung wird von der Betreuerin bzw. dem Betreuer der Bachelorarbeit bewertet. |
Assignment for the Following Curricula |
Computer Science: Core Qualification: Compulsory |
Module M1578: Seminars Computer Science |
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Courses | ||||||||||||
|
Module Responsible | Dozenten des SD E |
Admission Requirements | None |
Recommended Previous Knowledge |
Basic knowledge of Computer Science and Mathematics at the Bachelor's level. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students are able to
|
Skills |
The students are able to
|
Personal Competence | |
Social Competence |
The students are able to
|
Autonomy |
The students are able to
|
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Presentation |
Examination duration and scale | x |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Data Science: Elective Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Data Science: Elective Compulsory Engineering Science: Specialisation Information and Communication Systems: Elective Compulsory Computer Science in Engineering: Core Qualification: Compulsory |
Course L2362: Introductory Seminar Computer Science I |
Typ | Seminar |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Dozenten des SD E |
Language | DE/EN |
Cycle |
WiSe/ |
Content | |
Literature |
Course L2361: Introductory Seminar Computer Science II |
Typ | Seminar |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Dozenten des SD E |
Language | DE/EN |
Cycle |
WiSe/ |
Content | |
Literature |
Module M1754: Practical module 5 (dual study program, Bachelor's degree) |
||||||||
Courses | ||||||||
|
Module Responsible | Dr. Henning Haschke |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Dual students …
|
Skills |
Dual students …
|
Personal Competence | |
Social Competence |
Dual students …
|
Autonomy |
Dual students …
|
Workload in Hours | Independent Study Time 180, Study Time in Lecture 0 |
Credit points | 6 |
Course achievement | None |
Examination | Written elaboration |
Examination duration and scale | Documentation accompanying studies and across semesters: Module credit points are earned by completing a digital learning and development report (e-portfolio). This documents and reflects individual learning experiences and skills development relating to interlinking theory and practice, as well as professional practice. In addition, the partner company provides proof to the dual@TUHH Coordination Office that the dual student has completed the practical phase. |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory |
Course L2883: Practical term 5 (dual study program, Bachelor's degree) |
Typ | |
Hrs/wk | 0 |
CP | 6 |
Workload in Hours | Independent Study Time 180, Study Time in Lecture 0 |
Lecturer | Dr. Henning Haschke |
Language | DE |
Cycle | WiSe |
Content |
Company onboarding process
Operational knowledge and skills
Sharing/reflecting on learning
|
Literature |
|
Specialization I. Computer and Software Engineering
Module M1586: Scientific Programming |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Tobias Knopp |
Admission Requirements | None |
Recommended Previous Knowledge | procedural programming, linear algebra |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students
|
Skills |
Students are able
|
Personal Competence | |
Social Competence |
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. |
Autonomy |
Students are able to independently investigate a complex problem and assess which competencies are required to solve it. |
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Credit points | 6 |
Course achievement | None |
Examination | Subject theoretical and practical work |
Examination duration and scale | exercise task, group project with presentation, and written test |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Data Science: Elective Compulsory Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Data Science: Elective Compulsory Mechatronics: Specialisation Dynamic Systems and AI: Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L2405: Scientific Programming |
Typ | Lecture |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Tobias Knopp |
Language | DE/EN |
Cycle | SoSe |
Content |
|
Literature |
Ben Lauwens, Allen Downey: Think Julia: How to Think Like a Computer Scientist |
Course L2406: Scientific Programming |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Tobias Knopp |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1595: Machine Learning I |
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Courses | ||||||||||||
|
Module Responsible | Prof. Nihat Ay | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge | Linear Algebra, Analysis, Basic Programming Course | ||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
The students know
|
||||||||
Skills |
The students can
|
||||||||
Personal Competence | |||||||||
Social Competence |
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. |
||||||||
Autonomy |
Students are able to independently investigate a complex problem and assess which competencies are required to solve it. |
||||||||
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 90 min | ||||||||
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Theoretical Mechanical Engineering: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Data Science: Compulsory Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Advanced Materials: Elective Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory Engineering Science: Specialisation Data Science: Compulsory Engineering Science: Specialisation Mechanical Engineering: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Logistics and Mobility: Specialisation Information Technology: Elective Compulsory Mechanical Engineering: Specialisation Theoretical Mechanical Engineering: Elective Compulsory Mechatronics: Specialisation Dynamic Systems and AI: Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation Information Technology: Elective Compulsory |
Course L2432: Machine Learning I |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Nihat Ay |
Language | DE/EN |
Cycle | SoSe |
Content |
|
Literature |
|
Course L2433: Machine Learning I |
Typ | Recitation Section (small) |
Hrs/wk | 3 |
CP | 3 |
Workload in Hours | Independent Study Time 48, Study Time in Lecture 42 |
Lecturer | Prof. Nihat Ay |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1908: Fundamentals of Operating Systems |
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Courses | ||||||||||||
|
Module Responsible | Prof. Christian Dietrich |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
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. |
Skills |
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. |
Personal Competence | |
Social Competence |
Students are able to discuss and collaboratively present a problem in small groups with reference to operating systems and systems software. |
Autonomy |
Students are able to independently prepare and review the lecture content. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L3148: Fundamentals of Operating Systems |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Christian Dietrich |
Language | DE/EN |
Cycle | SoSe |
Content |
|
Literature |
|
Course L3149: Fundamentals of Operating Systems |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Christian Dietrich |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0791: Computer Architecture |
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Courses | ||||||||||||||||
|
Module Responsible | Prof. Heiko Falk | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
Module "Computer Engineering" |
||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
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. |
||||||||
Skills |
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. |
||||||||
Personal Competence | |||||||||
Social Competence |
Students are able to solve similar problems alone or in a group and to present the results accordingly. |
||||||||
Autonomy |
Students are able to acquire new knowledge from specific literature and to associate this knowledge with other classes. |
||||||||
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 90 minutes, contents of course and 4 attestations from the PBL "Computer architecture" | ||||||||
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Aircraft Systems Engineering: Core Qualification: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Aeronautics: Core Qualification: Elective Compulsory Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory |
Course L0793: Computer Architecture |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Heiko Falk |
Language | EN |
Cycle | WiSe |
Content |
The theoretical tutorials amplify the lecture's content by solving and discussing exercise sheets and thus serve as exam preparation. Practical aspects of computer architecture are taught in the FPGA-based PBL on computer architecture whose attendance is mandatory. |
Literature |
|
Course L0794: Computer Architecture |
Typ | Project-/problem-based Learning |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Heiko Falk |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L1864: Computer Architecture |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Heiko Falk |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0953: Introduction to Information Security |
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Courses | ||||||||||||
|
Module Responsible | Prof. Riccardo Scandariato | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge | Basics of Computer Science | ||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
Students can
|
||||||||
Skills |
Students can
|
||||||||
Personal Competence | |||||||||
Social Competence | Students are capable of appreciating the impact of security problems on those affected and of the potential responsibilities for their resolution. | ||||||||
Autonomy | None | ||||||||
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 120 minutes | ||||||||
Assignment for the Following Curricula |
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Information and Communication Systems: Compulsory |
Course L1114: Introduction to Information Security |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Riccardo Scandariato |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
Ross Anderson: Security Engineering, Wiley & Sons, 3rd edition, 2020 |
Course L1115: Introduction to Information Security |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Riccardo Scandariato |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1593: Data Mining |
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Courses | ||||||||||||
|
Module Responsible | Prof. Stefan Schulte | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
|
||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
After successful completion of the course, students know:
|
||||||||
Skills |
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. |
||||||||
Personal Competence | |||||||||
Social Competence |
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. |
||||||||
Autonomy |
Students are able to independently investigate a complex problem and assess which competencies are required to solve it. |
||||||||
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 90 min | ||||||||
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Data Science: Compulsory Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Data Science: Compulsory Logistics and Mobility: Specialisation Information Technology: Elective Compulsory Mechatronics: Specialisation Dynamic Systems and AI: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. Information Technology: Elective Compulsory |
Course L2434: Data Mining |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Stefan Schulte, Dr. Dominik Schallmoser |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
Charu C. Aggarwal: Text Mining - The Textbook, Springer, 2015. Available at https://link.springer.com/book/10.1007/978-3-319-14142-8 |
Course L2435: Data Mining |
Typ | Project-/problem-based Learning |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Stefan Schulte, Dr. Dominik Schallmoser |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1976: Embedded GPU Projects |
||||||||
Courses | ||||||||
|
Module Responsible | Prof. Sohan Lal |
Admission Requirements | None |
Recommended Previous Knowledge |
An introductory module on computer engineering or computer architecture, and good programming skills in Python/C++. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
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. |
Skills |
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. |
Personal Competence | |
Social Competence |
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. |
Autonomy |
Students learn to take ownership of individual and collective tasks within the team; fulfilling commitments and delivering quality work on time. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written elaboration |
Examination duration and scale | Report on achieved results |
Assignment for the Following Curricula |
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory |
Course L3224: Embedded GPU Projects |
Typ | Project-/problem-based Learning |
Hrs/wk | 4 |
CP | 6 |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Lecturer | Prof. Sohan Lal |
Language | EN |
Cycle | WiSe |
Content |
This is a project-based learning module where students will work with embedded boards encompassing GPUs, such as Jetson boards from NVIDIA to work on cutting-edge projects from various domains such as space and autonomous driving. An introduction to embedded boards including a set of projects will be proposed at the beginning of the module. It is also possible for a student to propose his/her project in consultation with the lecturer. The students will be encouraged to work in small teams (1-3 team members). The team-based approach not only facilitates a supportive learning environment but also equips students with the ability to tackle complex problems synergistically. By the end of this module, students will have mastered the intricacies of embedded boards encompassing GPUs and gained invaluable experience in real-world project development within the dynamic fields of space and autonomous driving. |
Literature |
|
Module M0754: Compiler Construction |
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Courses | ||||||||||||
|
Module Responsible | Prof. Sibylle Schupp |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
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. |
Skills |
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. |
Personal Competence | |
Social Competence |
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. |
Autonomy |
Students develop their software independently and define milestones by themselves. They receive feedback throughout the entire project. They organize the software project so that they can assess their progress themselves. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Subject theoretical and practical work |
Examination duration and scale | Software (Compiler) |
Assignment for the Following Curricula |
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L0703: Compiler Construction |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
Alfred Aho, Jeffrey Ullman, Ravi Sethi, and Monica S. Lam, Compilers: Principles, Techniques, and Tools, 2nd edition Aarne Ranta, Implementing Programming Languages, An Introduction to Compilers and Interpreters, with an appendix coauthored by Markus Forsberg, College Publications, London, 2012 |
Course L0704: Compiler Construction |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 4 |
Workload in Hours | Independent Study Time 92, Study Time in Lecture 28 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1300: Software Development |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Sibylle Schupp |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students explain the fundamental concepts of agile methods, describe the process of |
Skills |
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 |
Personal Competence | |
Social Competence |
Students discuss different design decisions in a group. They defend their solutions orally. They communicate in English. |
Autonomy |
Using accompanying tools, students can assess their level of knowledge continuously and adjust it appropriately. Within limits, they can set their own learning goals. Upon successful completion, students can identify and formulate concrete problems of software systems and propose solutions. Within this field, they can conduct independent studies to acquire the necessary competencies. They can devise plans to arrive at new solutions or assess existing ones. |
Workload in Hours | Independent Study Time 138, Study Time in Lecture 42 |
Credit points | 6 |
Course achievement | None |
Examination | Subject theoretical and practical work |
Examination duration and scale | Software |
Assignment for the Following Curricula |
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory |
Course L1790: Software Development |
Typ | Project-/problem-based Learning |
Hrs/wk | 2 |
CP | 5 |
Workload in Hours | Independent Study Time 122, Study Time in Lecture 28 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
Duvall, Paul M. Continuous Integration. Pearson Education India, 2007. Martin, Robert Cecil. Agile software development: principles, patterns, and practices. Prentice Hall PTR, 2003. http://scrum-kompakt.de/ Myers, Glenford J., Corey Sandler, and Tom Badgett. The art of software testing. John Wiley & Sons, 2011. |
Course L1789: Software Development |
Typ | Lecture |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
Duvall, Paul M. Continuous Integration. Pearson Education India, 2007. Martin, Robert Cecil. Agile software development: principles, patterns, and practices. Prentice Hall PTR, 2003. http://scrum-kompakt.de/ Myers, Glenford J., Corey Sandler, and Tom Badgett. The art of software testing. John Wiley & Sons, 2011. |
Module M0803: Embedded Systems |
||||||||||||||||
Courses | ||||||||||||||||
|
Module Responsible | Prof. Heiko Falk | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge | Computer Engineering | ||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
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. |
||||||||
Skills |
After having attended the course, students shall be able to realize simple embedded systems. The students shall realize which relevant parts of technological competences to use in order to obtain a functional embedded systems. In particular, they shall be able to compare different models of computations and feasible techniques for system-level design. They shall be able to judge in which areas of embedded system design specific risks exist. |
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Personal Competence | |||||||||
Social Competence |
Students are able to solve similar problems alone or in a group and to present the results accordingly. |
||||||||
Autonomy |
Students are able to acquire new knowledge from specific literature and to associate this knowledge with other classes. |
||||||||
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 90 minutes, contents of course and labs | ||||||||
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Electrical Engineering: Core Qualification: Elective Compulsory Engineering Science: Specialisation Electrical Engineering: Elective Compulsory Engineering Science: Specialisation Information and Communication Systems: Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory Aircraft Systems Engineering: Core Qualification: Elective Compulsory General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Elective Compulsory Computer Science in Engineering: Core Qualification: Compulsory Aeronautics: Core Qualification: Elective Compulsory Mechatronics: Core Qualification: Elective Compulsory Mechatronics: Specialisation Naval Engineering: Compulsory Mechatronics: Specialisation Electrical Systems: Compulsory Mechatronics: Specialisation Dynamic Systems and AI: Compulsory Mechatronics: Specialisation Robot- and Machine-Systems: Compulsory Mechatronics: Specialisation Medical Engineering: Compulsory Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory |
Course L0805: Embedded Systems |
Typ | Lecture |
Hrs/wk | 3 |
CP | 3 |
Workload in Hours | Independent Study Time 48, Study Time in Lecture 42 |
Lecturer | Prof. Heiko Falk |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
|
Course L2938: Embedded Systems |
Typ | Project-/problem-based Learning |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Heiko Falk |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
|
Course L0806: Embedded Systems |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Heiko Falk |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Specialization II. Mathematics and Engineering Science
Module M1730: Mathematics IV (EN) |
||||||||||||||||||||||||||||
Courses | ||||||||||||||||||||||||||||
|
Module Responsible | Prof. Marko Lindner |
Admission Requirements | None |
Recommended Previous Knowledge |
Mathematics I - III (EN or DE) |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
|
Skills |
|
Personal Competence | |
Social Competence |
|
Autonomy |
|
Workload in Hours | Independent Study Time 68, Study Time in Lecture 112 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 120 min |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Advanced Materials: Compulsory Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Data Science: Core Qualification: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Engineering Science: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory Engineering Science: Specialisation Advanced Materials: Compulsory Engineering Science: Specialisation Mechatronics: Compulsory Engineering Science: Specialisation Biomedical Engineering: Compulsory Engineering Science: Specialisation Electrical Engineering: Compulsory |
Course L2783: Differential Equations 2 (Partial Differential Equations) (EN) |
Typ | Lecture |
Hrs/wk | 2 |
CP | 1 |
Workload in Hours | Independent Study Time 2, Study Time in Lecture 28 |
Lecturer | Dozenten des Fachbereiches Mathematik der UHH |
Language | EN |
Cycle | SoSe |
Content |
Main features of the theory and numerical treatment of partial differential equations
|
Literature |
http://www.math.uni-hamburg.de/teaching/export/tuhh/index.html |
Course L2784: Differential Equations 2 (Partial Differential Equations) (EN) |
Typ | Recitation Section (large) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Dozenten des Fachbereiches Mathematik der UHH |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L2785: Differential Equations 2 (Partial Differential Equations) (EN) |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Dozenten des Fachbereiches Mathematik der UHH |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L2786: Complex Functions (EN) |
Typ | Lecture |
Hrs/wk | 2 |
CP | 1 |
Workload in Hours | Independent Study Time 2, Study Time in Lecture 28 |
Lecturer | Dozenten des Fachbereiches Mathematik der UHH |
Language | EN |
Cycle | SoSe |
Content |
Main features of complex analysis
|
Literature |
http://www.math.uni-hamburg.de/teaching/export/tuhh/index.html |
Course L2787: Complex Functions (EN) |
Typ | Recitation Section (large) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Dozenten des Fachbereiches Mathematik der UHH |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L2788: Complex Functions (EN) |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Dozenten des Fachbereiches Mathematik der UHH |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1962: Basics space electronics and primary mission |
||||||||
Courses | ||||||||
|
Module Responsible | Prof. Ulf Kulau |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
|
Skills |
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.
|
Personal Competence | |
Social Competence |
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. |
Autonomy |
After completing the module, students will be able to independently plan and carry out scientific projects and processes. In group work, organization, idea generation, derivation of hypotheses and thought processes are to be independently moderated and carried out. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written elaboration |
Examination duration and scale | Report on achieved results |
Assignment for the Following Curricula |
Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Electrical Engineering: Core Qualification: Elective Compulsory Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory |
Course L3204: Basics space electronics and primary mission |
Typ | Project-/problem-based Learning |
Hrs/wk | 4 |
CP | 6 |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Lecturer | Prof. Ulf Kulau |
Language | DE/EN |
Cycle |
WiSe/ |
Content | |
Literature |
Module M0651: Computational Geometry |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Dr. Prashant Batra |
Admission Requirements | None |
Recommended Previous Knowledge |
Linear algebra and analytic geometry as taught in higher secondary school (Computing with vectors a. determinants, Interpretation of scalar product, cross-product, Representation of lines/planes, Satz d. Pythagoras' theorem, cosine theorem, Thales' theorem, projections/embeddings) Basic data structures (trees, binary trees, search trees, balanced binary trees, linked lists) Definition of a graph |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
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. |
Skills |
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. |
Personal Competence | |
Social Competence |
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. |
Autonomy |
Students are capable of accessing independently further logical connections between the concepts about which they have learnt and are able to verify them. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory |
Course L0393: Computational Geoemetry |
Typ | Lecture | ||||||||||||||||||||||||
Hrs/wk | 2 | ||||||||||||||||||||||||
CP | 4 | ||||||||||||||||||||||||
Workload in Hours | Independent Study Time 92, Study Time in Lecture 28 | ||||||||||||||||||||||||
Lecturer | Dr. Prashant Batra | ||||||||||||||||||||||||
Language | DE | ||||||||||||||||||||||||
Cycle | WiSe | ||||||||||||||||||||||||
Content |
Construction of the convex hull of n points, triangulation of a simple polygon
Construction of Delaunay-triangulation and Voronoi-diagram Algorithms and data structures for the construction of arrangements, and Ham-Sandwich-Cuts. the intersection of half-planes, the optimization of a linear functional over the latter. Efficiente determination of all intersection of (orthogonal) lines (line segments) Approximative computation of the diameter of a point set Randomised incremental algorithms Basics of lattice point theory , LLL-algorithm and application in integer-valued optimization. Basics of motion planning |
||||||||||||||||||||||||
Literature |
Computational Geometry Algorithms and Applications Authors:
Springer e-Book: http://dx.doi.org/10.1007/978-3-540-77974-2
Springer e-Book: http://dx.doi.org/10.1007/3-540-27619-X O’Rourke, Joseph ISBN: 0-521-44034-3 ; 0-521-44592-2
Devadoss, Satyan L.; O’Rourke, Joseph
|
Course L0394: Computational Geoemetry |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Dr. Prashant Batra |
Language | DE |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0941: Combinatorial Structures and Algorithms |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Anusch Taraz |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
|
Skills |
|
Personal Competence | |
Social Competence |
|
Autonomy |
|
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Oral exam |
Examination duration and scale | 30 min |
Assignment for the Following Curricula |
Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory |
Course L1100: Combinatorial Structures and Algorithms |
Typ | Lecture |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Anusch Taraz, Dr. Dennis Clemens |
Language | DE/EN |
Cycle | WiSe |
Content |
|
Literature |
|
Course L1101: Combinatorial Structures and Algorithms |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Anusch Taraz |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M2046: Introduction to Quantum Computing |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Martin Kliesch | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
|
||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
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. |
||||||||
Skills |
|
||||||||
Personal Competence | |||||||||
Social Competence |
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. |
||||||||
Autonomy |
After completion of this module, students are able to work out
sub-areas of the subject independently using textbooks
and other literature, to summarize and present the acquired knowledge
and to link it to the contents of other courses. |
||||||||
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 120 min | ||||||||
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Data Science: Elective Compulsory Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Engineering Science: Specialisation Data Science: Elective Compulsory Engineering Science: Specialisation Information and Communication Systems: Elective Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L3109: Introduction to Quantum Computing |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Martin Kliesch |
Language | EN |
Cycle | WiSe |
Content |
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. |
Literature |
|
Course L3110: Introduction to Quantum Computing |
Typ | Recitation Section (large) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Martin Kliesch |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1592: Statistics |
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Courses | ||||||||||||||||
|
Module Responsible | Prof. Matthias Schulte | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
Stochastics (or a comparable class) |
||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
|
||||||||
Skills |
|
||||||||
Personal Competence | |||||||||
Social Competence |
|
||||||||
Autonomy |
|
||||||||
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 90 min | ||||||||
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Advanced Materials: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Data Science: Compulsory Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Advanced Materials: Elective Compulsory Engineering Science: Specialisation Data Science: Compulsory Engineering Science: Specialisation Information and Communication Systems: Compulsory Logistics and Mobility: Specialisation Information Technology: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. Information Technology: Elective Compulsory |
Course L2430: Statistics |
Typ | Lecture |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Matthias Schulte |
Language | DE/EN |
Cycle | WiSe |
Content |
|
Literature |
|
Course L3229: Statistics |
Typ | Project-/problem-based Learning |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Matthias Schulte |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L2431: Statistics |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Matthias Schulte |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0715: Solvers for Sparse Linear Systems |
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Courses | ||||||||||||
|
Module Responsible | Prof. Sabine Le Borne |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can
|
Skills |
Students are able to
|
Personal Competence | |
Social Competence |
Students are able to
|
Autonomy |
Students are capable
|
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Oral exam |
Examination duration and scale | 20 min |
Assignment for the Following Curricula |
Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory |
Course L0583: Solvers for Sparse Linear Systems |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Sabine Le Borne |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
|
Course L0584: Solvers for Sparse Linear Systems |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Sabine Le Borne |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0668: Algebra and Control |
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Courses | ||||||||||||
|
Module Responsible | Dr. Prashant Batra |
Admission Requirements | None |
Recommended Previous Knowledge |
Basics of Real Analysis and Linear Algebra of Vector Spaces and either of: Introduction to Control Theory or: Discrete Mathematics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can
|
Skills |
Students are able to
|
Personal Competence | |
Social Competence | After completing the module, students are able to solve subject-related tasks and to present the results. |
Autonomy | Students are provided with tasks which are exam-related so that they can examine their learning progress and reflect on it. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Oral exam |
Examination duration and scale | 30 min |
Assignment for the Following Curricula |
Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L0428: Algebra and Control |
Typ | Lecture |
Hrs/wk | 2 |
CP | 4 |
Workload in Hours | Independent Study Time 92, Study Time in Lecture 28 |
Lecturer | Dr. Prashant Batra |
Language | DE/EN |
Cycle | SoSe |
Content |
- Algebraic control methods, polynomial and fractional approach
- Parametrization of all stabilizing controllers - Selected methods of pole assignment. - Filtering and sensitivity minimization - Euclidean algorithm, diophantine equations over rings - Smith-McMillan normal form |
Literature |
|
Course L0429: Algebra and Control |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Dr. Prashant Batra |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0634: Introduction into Medical Technology and Systems |
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Courses | ||||||||||||||||
|
Module Responsible | Prof. Alexander Schlaefer | ||||||||||||
Admission Requirements | None | ||||||||||||
Recommended Previous Knowledge |
principles of math (algebra, analysis/calculus) |
||||||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||||||
Professional Competence | |||||||||||||
Knowledge |
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. |
||||||||||||
Skills |
The students are able to evaluate systems and medical devices in the context of clinical applications. |
||||||||||||
Personal Competence | |||||||||||||
Social Competence |
The students describe a problem in medical technology
as a project, and define tasks that are solved in a joint effort. |
||||||||||||
Autonomy |
The students can assess their level of knowledge and document their work results. They can critically evaluate the results achieved and present them in an appropriate manner. |
||||||||||||
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 | ||||||||||||
Credit points | 6 | ||||||||||||
Course achievement |
|
||||||||||||
Examination | Written exam | ||||||||||||
Examination duration and scale | 90 minutes | ||||||||||||
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Data Science: Specialisation II. Application: Elective Compulsory Electrical Engineering: Core Qualification: Elective Compulsory Engineering Science: Specialisation Biomedical Engineering: Compulsory General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory International Management and Engineering: Specialisation II. Medical Engineering: Elective Compulsory International Management and Engineering: Specialisation II. Medical Engineering: Elective Compulsory Mechatronics: Specialisation Medical Engineering: Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory |
Course L0342: Introduction into Medical Technology and Systems |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Alexander Schlaefer |
Language | DE |
Cycle | SoSe |
Content |
- imaging systems |
Literature |
Bernhard Priem, "Visual Computing for Medicine", 2014 |
Course L0343: Introduction into Medical Technology and Systems |
Typ | Project Seminar |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Alexander Schlaefer |
Language | DE |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L1876: Introduction into Medical Technology and Systems |
Typ | Recitation Section (large) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Alexander Schlaefer |
Language | DE |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1269: Lab Cyber-Physical Systems |
||||||||
Courses | ||||||||
|
Module Responsible | Prof. Heiko Falk |
Admission Requirements | None |
Recommended Previous Knowledge | Module "Embedded Systems" |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
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. |
Skills | After successful attendance of the lab, students are able to develop simple CPS. They understand the interdependencies between a CPS and its surrounding processes which stem from the fact that a CPS interacts with the environment via sensors, A/D converters, digital processors, D/A converters and actors. The lab enables students to compare modelling approaches, to evaluate their advantages and limitations, and to decide which technique to use for a concrete task. They will be able to apply these techniques to practical problems. They obtain first experiences in hardware-related software development, in industry-relevant specification tools and in the area of simple control applications. |
Personal Competence | |
Social Competence |
Students are able to solve similar problems alone or in a group and to present the results accordingly. |
Autonomy |
Students are able to acquire new knowledge from specific literature and to associate this knowledge with other classes. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written elaboration |
Examination duration and scale | Execution and documentation of all lab experiments |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory Mechatronics: Core Qualification: Elective Compulsory |
Course L1740: Lab Cyber-Physical Systems |
Typ | Project-/problem-based Learning |
Hrs/wk | 4 |
CP | 6 |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Lecturer | Prof. Heiko Falk |
Language | EN |
Cycle | SoSe |
Content |
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Literature |
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Module M0672: Signals and Systems |
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Module Responsible | Prof. Gerhard Bauch |
Admission Requirements | None |
Recommended Previous Knowledge |
Mathematics 1-3 The modul is an introduction to the theory of signals and systems. Good knowledge in maths as covered by the moduls Mathematik 1-3 is expected. Further experience with spectral transformations (Fourier series, Fourier transform, Laplace transform) is useful but not required. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students are able to classify and describe signals and linear time-invariant (LTI) systems using methods of signal and system theory. They are able to apply the fundamental transformations of continuous-time and discrete-time signals and systems. They can describe and analyse deterministic signals and systems mathematically in both time and image domain. In particular, they understand the effects in time domain and image domain which are caused by the transition of a continuous-time signal to a discrete-time signal. The students are familiar with the contents of lecture and tutorials. They can explain and apply them to new problems. |
Skills | The students are able to describe and analyse deterministic signals and linear time-invariant systems using methods of signal and system theory. They can analyse and design basic systems regarding important properties such as magnitude and phase response, stability, linearity etc.. They can assess the impact of LTI systems on the signal properties in time and frequency domain. |
Personal Competence | |
Social Competence | The students can jointly solve specific problems. |
Autonomy | The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system. |
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechanical Engineering: Specialisation Mechatronics: Elective Compulsory Mechatronics: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory |
Course L0432: Signals and Systems |
Typ | Lecture |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/EN |
Cycle | SoSe |
Content |
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Literature |
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Course L0433: Signals and Systems |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Specialization III. Subject Specific Focus
Module M1562: Technical Complementary Course I for CSBS |
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Courses | ||||
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Module Responsible | Dozenten des SD E |
Admission Requirements | None |
Recommended Previous Knowledge | |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge | |
Skills | |
Personal Competence | |
Social Competence | |
Autonomy | |
Workload in Hours | Depends on choice of courses |
Credit points | 6 |
Assignment for the Following Curricula |
Computer Science: Specialisation III. Subject Specific Focus: Elective Compulsory |
Module M1568: Technical Complementary Course II for CSBS |
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Courses | ||||
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Module Responsible | Dozenten des SD E |
Admission Requirements | None |
Recommended Previous Knowledge | |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge | |
Skills | |
Personal Competence | |
Social Competence | |
Autonomy | |
Workload in Hours | Depends on choice of courses |
Credit points | 6 |
Assignment for the Following Curricula |
Computer Science: Specialisation III. Subject Specific Focus: Elective Compulsory |
Thesis
Module M1800: Bachelor thesis (dual study program) |
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Courses | ||||
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Module Responsible | Professoren der TUHH |
Admission Requirements | None |
Recommended Previous Knowledge | |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Dual students…
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Skills |
Dual students…
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Personal Competence | |
Social Competence |
Dual students…
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Autonomy |
Dual students…
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Workload in Hours | Independent Study Time 360, Study Time in Lecture 0 |
Credit points | 12 |
Course achievement | None |
Examination | Thesis |
Examination duration and scale | According to General Regulations |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Thesis: Compulsory Civil- and Environmental Engineering: Thesis: Compulsory Chemical and Bioprocess Engineering: Thesis: Compulsory Computer Science: Thesis: Compulsory Data Science: Thesis: Compulsory Electrical Engineering: Thesis: Compulsory Engineering Science: Thesis: Compulsory Green Technologies: Energy, Water, Climate: Thesis: Compulsory Computer Science in Engineering: Thesis: Compulsory Mechanical Engineering: Thesis: Compulsory Mechatronics: Thesis: Compulsory Naval Architecture: Thesis: Compulsory Technomathematics: Thesis: Compulsory Engineering and Management - Major in Logistics and Mobility: Thesis: Compulsory |