Program description
Content
Core Qualification
Module M0577: Non-technical Courses for Bachelors |
Module Responsible | Dagmar Richter |
Admission Requirements | None |
Recommended Previous Knowledge | None |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The Non-technical
Academic Programms (NTA) imparts skills that, in view of the TUHH’s training profile, professional engineering studies require but are not able to cover fully. Self-reliance, self-management, collaboration and professional and personnel management competences. The department implements these training objectives in its teaching architecture, in its teaching and learning arrangements, in teaching areas and by means of teaching offerings in which students can qualify by opting for specific competences and a competence level at the Bachelor’s or Master’s level. The teaching offerings are pooled in two different catalogues for nontechnical complementary courses. The Learning Architecture consists of a cross-disciplinarily study offering. The centrally designed teaching offering ensures that courses in the nontechnical academic programms follow the specific profiling of TUHH degree courses. The learning architecture demands and trains independent educational planning as regards the individual development of competences. It also provides orientation knowledge in the form of “profiles” The subjects that can be studied in parallel throughout the student’s entire study program - if need be, it can be studied in one to two semesters. In view of the adaptation problems that individuals commonly face in their first semesters after making the transition from school to university and in order to encourage individually planned semesters abroad, there is no obligation to study these subjects in one or two specific semesters during the course of studies. Teaching and Learning Arrangements provide for students, separated into B.Sc. and M.Sc., to learn with and from each other across semesters. The challenge of dealing with interdisciplinarity and a variety of stages of learning in courses are part of the learning architecture and are deliberately encouraged in specific courses. Fields of Teaching are based on research findings from the academic disciplines cultural studies, social studies, arts, historical studies, migration studies, communication studies and sustainability research, and from engineering didactics. In addition, from the winter semester 2014/15 students on all Bachelor’s courses will have the opportunity to learn about business management and start-ups in a goal-oriented way. The fields of teaching are augmented by soft skills offers and a foreign language offer. Here, the focus is on encouraging goal-oriented communication skills, e.g. the skills required by outgoing engineers in international and intercultural situations. The Competence Level of the courses offered in this area is different as regards the basic training objective in the Bachelor’s and Master’s fields. These differences are reflected in the practical examples used, in content topics that refer to different professional application contexts, and in the higher scientific and theoretical level of abstraction in the B.Sc. This is also reflected in the different quality of soft skills, which relate to the different team positions and different group leadership functions of Bachelor’s and Master’s graduates in their future working life. Specialized Competence (Knowledge) Students can
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Skills |
Professional Competence (Skills) In selected sub-areas students can
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Personal Competence | |
Social Competence |
Personal Competences (Social Skills) Students will be able
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Autonomy |
Personal Competences (Self-reliance) Students are able in selected areas
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Workload in Hours | Depends on choice of courses |
Credit points | 6 |
Courses |
Information regarding lectures and courses can be found in the corresponding module handbook published separately. |
Module M0561: Discrete Algebraic Structures |
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Courses | ||||||||||||
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Module Responsible | Prof. Antoine Mottet |
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 |
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. Antoine Mottet |
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. Antoine Mottet |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
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
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Skills |
- Mastery of typical development tools |
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Personal Competence | |||||||||
Social Competence |
- After completing the module, students are able to work on subject-specific tasks, distribute work and present the results appropriately within a small group.
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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, |
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Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
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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 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 M1809: Introduction to Data Science |
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Courses | ||||||||||||
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Module Responsible | Prof. Pierre-Alexandre Murena |
Admission Requirements | None |
Recommended Previous Knowledge | none |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
In this course, students receive a broad overview of the scientific field known as Data Science. The basic terms and concepts are explained at a high level of abstraction and enable the students to classify the methods taught in the further course of study. In addition to a historical overview, current application examples of Data Science are presented. |
Skills |
Students are able to:
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Personal Competence | |
Social Competence |
Students are able to discuss and collaborate in small groups to present a topic related to Data Science. |
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 | Presentation |
Examination duration and scale | Preparation and presentation of a poster on a Data Science topic |
Assignment for the Following Curricula |
Data Science: Core Qualification: Compulsory Mechatronics: Specialisation Dynamic Systems and AI: Elective Compulsory |
Course L2998: Introduction to Data Science |
Typ | Lecture |
Hrs/wk | 2 |
CP | 4 |
Workload in Hours | Independent Study Time 92, Study Time in Lecture 28 |
Lecturer | Prof. Pierre-Alexandre Murena |
Language | DE |
Cycle | WiSe |
Content |
In this course, students receive a broad overview of the scientific field known as Data Science. The basic terms and concepts are explained at a high level of abstraction and enable the students to classify the methods taught in the further course of study. In addition to a historical overview, current application examples of Data Science are presented. |
Literature |
Christopher M. Bishop: Pattern Recognition and Machine Learning |
Course L2999: Introduction to Data Science |
Typ | Seminar |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Pierre-Alexandre Murena |
Language | DE |
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 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 |
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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. |
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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. |
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Personal Competence | |||||||||
Social Competence |
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Autonomy |
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Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 | ||||||||
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: 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 Mechatronics: Elective Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory Engineering Science: Specialisation Data Science: 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 M0727: Stochastics |
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Courses | ||||||||||||
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Module Responsible | Prof. Matthias Schulte |
Admission Requirements | None |
Recommended Previous Knowledge |
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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 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 |
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Literature |
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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 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 |
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 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 |
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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 |
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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 |
Analysis:
Linear Algebra:
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Literature |
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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 M0829: Foundations of Management |
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Courses | ||||||||||||
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Module Responsible | Prof. Christian Lüthje |
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
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Personal Competence | |
Social Competence |
Students are able to
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Autonomy |
Students are able to
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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 Chemical and Bioprocess Engineering: Specialisation Bio Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation Chemical Engineering: Elective Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Specialisation Biotechnologies: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Energy Systems / Renewable Energies: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Energy Technology: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Maritime Technologies: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Water Technologies: Elective 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: Specialisation Naval Engineering: Compulsory Mechatronics: Specialisation Electrical Systems: Compulsory Mechatronics: Specialisation Dynamic Systems and AI: Compulsory Mechatronics: Core Qualification: Compulsory Mechatronics: Specialisation Robot- and Machine-Systems: Compulsory Mechatronics: Specialisation Medical Engineering: 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. Christian Lüthje, 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. Christian Lüthje, Prof. Christian Ringle, Prof. Christoph Ihl, Prof. Cornelius Herstatt, Prof. Kathrin Fischer, Prof. Matthias Meyer, Prof. Thomas Wrona, Prof. Thorsten Blecker, Prof. Wolfgang Kersten |
Language | DE |
Cycle |
WiSe/ |
Content |
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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 M0625: Databases |
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Courses | ||||||||||||
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Module Responsible | Prof. Stefan Schulte |
Admission Requirements | None |
Recommended Previous Knowledge |
Students should have basic knowledge in the following areas:
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Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
After successful completion of the course, students know:
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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 Engineering Science: Specialisation Information and Communication Systems: Elective 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 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 M0662: Numerical Mathematics I |
||||||||||||
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 are able to
|
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 | Written exam |
Examination duration and scale | 90 minutes |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Theoretical Mechanical Engineering: Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Aircraft Systems Engineering: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Mechatronics: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Energy Systems: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Advanced Materials: Compulsory General Engineering Science (German program, 7 semester): Specialisation Data Science: Compulsory Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Elective Compulsory Engineering Science: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Specialisation Energy Technology: Elective Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechanical Engineering: Specialisation Theoretical Mechanical Engineering: Compulsory Mechanical Engineering: Specialisation Energy Systems: Elective Compulsory Mechanical Engineering: Specialisation Mechatronics: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course Core Studies: Elective Compulsory Process Engineering: Specialisation Process Engineering: Elective Compulsory |
Course L0417: Numerical Mathematics I |
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 | WiSe |
Content |
|
Literature |
|
Course L0418: Numerical Mathematics I |
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, Dr. Jens-Peter Zemke |
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 Engineering Science: Specialisation Information and Communication Systems: 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 II. 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 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 M1595: Machine Learning I |
||||||||||||
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 Data Science: Compulsory Engineering Science: Specialisation Mechanical Engineering: Elective Compulsory Engineering Science: Specialisation Information and Communication Systems: Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory Engineering Science: Specialisation Mechanical Engineering and Management: 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 II. 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 M1578: Seminars Computer Science |
||||||||||||
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 M0672: Signals and Systems |
||||||||||||
Courses | ||||||||||||
|
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 |
|
Literature |
|
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 |
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 Engineering Science: Specialisation Information and Communication Systems: 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 II. Traffic Planning and Systems: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. 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 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 M0953: Introduction to Information Security |
||||||||||||
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 M1594: Machine Learning II |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Nihat Ay | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
Successful participation in the modules:
|
||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
Students get to know tools used by development teams to
|
||||||||
Skills |
Students work in teams on a larger data project. The required competences are learned and practically applied. These are for example:
|
||||||||
Personal Competence | |||||||||
Social Competence |
Team work has its own challenges with respect to interaction of team members as well as finding the necessary agreement during joint software development. During the project students learn the required competences and experience the practical needs. |
||||||||
Autonomy |
During team work it is mandatory to take and explain a certain position, to independently complete assigned tasks, and to present results to the team. Open issues must be identified and returned into the team to find an agreed resolution. |
||||||||
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 Data Science: Elective Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Data Science: Elective Compulsory Mechatronics: Specialisation Dynamic Systems and AI: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L2436: Machine Learning II |
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 | WiSe |
Content |
|
Literature |
|
Course L2941: Machine Learning II |
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 | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1593: Data Mining |
||||||||||||
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:
|
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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. |
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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 M1620: Ethics in Information Technology |
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Courses | ||||||||||||
|
Module Responsible | Prof. Maximilian Kiener |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students are familiar with:
|
Skills |
The students can:
|
Personal Competence | |
Social Competence |
After completing the module, the students are able to work on subject-specific tasks independently or in groups and present them effectively. |
Autonomy |
After completing the module, the students are able to independently explore subfields of the subject area using specialized literature, summarize the acquired knowledge, present it, and integrate it with the content of other courses. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Presentation |
Examination duration and scale | - |
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 Technomathematics: Specialisation IV. Subject Specific Focus: Elective Compulsory |
Course L2450: Ethics in Information Technology |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Maximilian Kiener |
Language | DE/EN |
Cycle | SoSe |
Content | |
Literature |
Wird zu Beginn der Lehrveranstaltung bekannt gegeben. |
Course L2451: Ethics in Information Technology |
Typ | Seminar |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Maximilian Kiener |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Specialization I. Mathematics/Computer Science
Module M0834: Computernetworks and Internet Security |
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Courses | ||||||||||||
|
Module Responsible | Prof. Andreas Timm-Giel |
Admission Requirements | None |
Recommended Previous Knowledge |
Basic of Computer Science |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
In this course, 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 and lecture discussions, these basic principles and an introduction to performance modelling are addressed using exercises, homework assignments and labs. This comprises of:
|
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: Elective Compulsory Computer Science: Core Qualification: Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Electrical Engineering: Core Qualification: Elective Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory Engineering Science: Specialisation Electrical Engineering: Elective Compulsory Engineering Science: Specialisation Information and Communication Systems: 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 M0731: Functional Programming |
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Courses | ||||||||||||||||
|
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. |
||||||||
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. |
||||||||
Workload in Hours | Independent Study Time 96, Study Time in Lecture 84 | ||||||||
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 Engineering Science: Specialisation Information and Communication Systems: 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., 2nd edition 2016. |
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 M0941: Combinatorial Structures and Algorithms |
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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 M0675: Introduction to Communications and Random Processes |
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Courses | ||||||||||||||||
|
Module Responsible | Prof. Gerhard Bauch |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students know and understand the fundamental building blocks of a communications system. They can describe and analyse the individual building blocks using knowledge of signal and system theory as well as the theory of stochastic processes. The are aware of the essential resources and evaluation criteria of information transmission and are able to design and evaluate a basic communications system. 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 design and evaluate a basic communications system. In particular, they can estimate the required resources in terms of bandwidth and power. They are able to assess essential evaluation parameters of a basic communications system such as bandwidth efficiency or bit error rate and to decide for a suitable transmission method. |
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): Specialisation Electrical Engineering: Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Specialisation Information and Communication Systems: Elective Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechatronics: Specialisation Electrical Systems: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory |
Course L0442: Introduction to Communications and Random Processes |
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 | WiSe |
Content |
|
Literature |
K. Kammeyer: Nachrichtenübertragung, Teubner P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner. M. Bossert: Einführung in die Nachrichtentechnik, Oldenbourg. J.G. Proakis, M. Salehi: Grundlagen der Kommunikationstechnik. Pearson Studium. J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill. S. Haykin: Communication Systems. Wiley J.G. Proakis, M. Salehi: Communication Systems Engineering. Prentice-Hall. J.G. Proakis, M. Salehi, G. Bauch, Contemporary Communication Systems. Cengage Learning. |
Course L0443: Introduction to Communications and Random Processes |
Typ | Recitation Section (large) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L2354: Introduction to Communications and Random Processes |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1615: Introduction to Data Acquisition and Processing |
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Courses | ||||||||||||||||
|
Module Responsible | Prof. Alexander Schlaefer | ||||||||||||
Admission Requirements | None | ||||||||||||
Recommended Previous Knowledge |
principles of mathematics sound programming skills basic principles of electrical engineering / physics |
||||||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||||||
Professional Competence | |||||||||||||
Knowledge |
The students are able to explain the purpose of metrology and the acquisition and processing of measurements. They can detail aspects of probability theory and errors, and explain the processing of stochastic signals. Students know methods to digitalize and describe measured signals. Data processing from acquisition to regression and classification can be described in context. |
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Skills |
The students are able to evaluate problems of metrology and to apply methods for describing and processing of measurements. |
||||||||||||
Personal Competence | |||||||||||||
Social Competence |
The students solve problems in small groups. An actual problem including data acquisition and data processing is solved in groups. |
||||||||||||
Autonomy |
The students can reflect their knowledge and discuss and evaluate their results. |
||||||||||||
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 Data Science: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Mechatronics: Specialisation Medical Engineering: Compulsory |
Course L2445: Data Acquisition and Data Processing |
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 | WiSe |
Content |
Within an actual project setting, relevant tasks in data acquisition and data processing willbe discussed, including - data acquisition (e.g., image data, sensor data) - data pre-processing (e.g., filtering) - data analysis (e.g., solving regressing and classification tasks using machine learning methods) - evaluation and interpretation of the results |
Literature |
Puente León, Kiencke: Messtechnik, Springer 2012 |
Course L0779: Measurements: Methods and Data Processing |
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 | WiSe |
Content |
introduction, systems and errors in metrology, probability theory, stochastic processes, Bayes and Kalman filter, acquisition of analog signals, applied metrology, regression, interpolation, and classification based on measurements |
Literature |
Puente León, Kiencke: Messtechnik, Springer 2012 |
Course L0780: Measurements: Methods and Data Processing |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Alexander Schlaefer |
Language | DE |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0730: Computer Engineering |
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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: Specialisation I. Mathematics/Computer Science: Elective Compulsory Electrical Engineering: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: 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 M1598: Image Processing |
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Courses | ||||||||||||
|
Module Responsible | Prof. Tobias Knopp |
Admission Requirements | None |
Recommended Previous Knowledge | Signal and Systems |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students know about
|
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 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: Intelligence Engineering: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Data Science: Specialisation IV. Special Focus Area: Elective Compulsory Data Science: Specialisation II. Computer Science: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Core Qualification: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory |
Course L2443: Image Processing |
Typ | Lecture |
Hrs/wk | 2 |
CP | 4 |
Workload in Hours | Independent Study Time 92, Study Time in Lecture 28 |
Lecturer | Prof. Tobias Knopp |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
Bredies/Lorenz, Mathematische Bildverarbeitung, Vieweg, 2011 |
Course L2444: Image Processing |
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 | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M2046: Introduction to Quantum Computing |
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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 M0562: Computability and Complexity Theory |
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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 |
To goal is this course is to gain some basic understanding of the limits of computation and, in particular, knowledge and understanding of the topics of the associated Lehrveranstaltungen. |
||||||||
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 | 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: 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 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 | EN |
Cycle | SoSe |
Content |
|
Literature |
Michael Sipser, Introduction to the Theory of Computation |
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 | EN |
Cycle | SoSe |
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 M1730: Mathematics IV (EN) |
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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: Specialisation I. Mathematics/Computer Science: Elective Compulsory Engineering Science: Specialisation Advanced Materials: Compulsory Engineering Science: Specialisation Mechatronics: Compulsory Engineering Science: Specialisation Biomedical Engineering: Compulsory Engineering Science: Specialisation Electrical Engineering: Compulsory Engineering Science: Specialisation Information and Communication Systems: Compulsory Engineering Science: Specialisation Mechanical Engineering and Management: Elective 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 M0732: Software Engineering |
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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 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. |
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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 Engineering Science: Specialisation Information and Communication Systems: 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 |
Ravi Sethi, Software Engineering. Basic Principles and Best Practices. Cambridge University Press 2022. 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 M1922: Technical Complementary Course for DSBS (according to Subject Specific Regulations) |
||||
Courses | ||||
|
Module Responsible | Prof. Tobias Knopp |
Admission Requirements | None |
Recommended Previous Knowledge |
See selected module according to Subject Specific Regulations |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
See selected module according to Subject Specific Regulations |
Skills |
See selected module according to Subject Specific Regulations |
Personal Competence | |
Social Competence |
See selected module according to Subject Specific Regulations |
Autonomy |
See selected module according to Subject Specific Regulations |
Workload in Hours | Depends on choice of courses |
Credit points | 6 |
Assignment for the Following Curricula |
Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory |
Module M1977: Logic in Computer Science |
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Courses | ||||||||||||
|
Module Responsible | Prof. Antoine Mottet |
Admission Requirements | None |
Recommended Previous Knowledge | Automata theory and formal languages |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students know:
|
Skills | Students are able to employ the language of logic to formalize specifications of information systems. |
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 | 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 I. Computer Science: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L3225: Logic in Computer Science |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Antoine Mottet |
Language | EN |
Cycle | SoSe |
Content |
This course will cover some topics from mathematical logic that are relevant for computer scientists. These topics include for example:
|
Literature |
|
Course L3232: Logic in Computer Science |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Antoine Mottet |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Specialization II. Application
Module M0933: Fundamentals of Materials Science |
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Courses | ||||||||||||||||
|
Module Responsible | Prof. Jörg Weißmüller |
Admission Requirements | None |
Recommended Previous Knowledge |
Highschool-level physics, chemistry und mathematics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students have acquired a fundamental knowledge on metals, ceramics and polymers and can describe this knowledge comprehensively. Fundamental knowledge here means specifically the issues of atomic structure, microstructure, phase diagrams, phase transformations, corrosion and mechanical properties. The students know about the key aspects of characterization methods for materials and can identify relevant approaches for characterizing specific properties. They are able to trace materials phenomena back to the underlying physical and chemical laws of nature. |
Skills |
The students are able to trace materials phenomena back to the underlying physical and chemical laws of nature. Materials phenomena here refers to mechanical properties such as strength, ductility, and stiffness, chemical properties such as corrosion resistance, and to phase transformations such as solidification, precipitation, or melting. The students can explain the relation between processing conditions and the materials microstructure, and they can account for the impact of microstructure on the material’s behavior. |
Personal Competence | |
Social Competence | - |
Autonomy | - |
Workload in Hours | Independent Study Time 96, Study Time in Lecture 84 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 180 min |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering: Compulsory General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory General Engineering Science (German program, 7 semester): Specialisation Naval Architecture: Compulsory General Engineering Science (German program, 7 semester): Specialisation Advanced Materials: Compulsory Data Science: Specialisation II. Application: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Energy Technology: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Maritime Technologies: Elective Compulsory Logistics and Mobility: Specialisation Production Management and Processes: Elective Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. Production Management and Processes: Elective Compulsory |
Course L1085: Fundamentals of Materials Science I |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Jörg Weißmüller |
Language | DE |
Cycle | WiSe |
Content | |
Literature |
Vorlesungsskript W.D. Callister: Materials Science and Engineering - An Introduction. 5th ed., John Wiley & Sons, Inc., New York, 2000, ISBN 0-471-32013-7 P. Haasen: Physikalische Metallkunde. Springer 1994 |
Course L0506: Fundamentals of Materials Science II (Advanced Ceramic Materials, Polymers and Composites) |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Bodo Fiedler, Prof. Gerold Schneider |
Language | DE |
Cycle | WiSe |
Content | Chemische Bindungen und Aufbau von Festkörpern; Kristallaufbau; Werkstoffprüfung; Schweißbarkeit; Herstellung von Keramiken; Aufbau und Eigenschaften der Keramik; Herstellung, Aufbau und Eigenschaften von Gläsern; Polymerwerkstoffe, Makromolekularer Aufbau; Struktur und Eigenschaften der Polymere; Polymerverarbeitung; Verbundwerkstoffe |
Literature |
Vorlesungsskript W.D. Callister: Materials Science and Engineering -An Introduction-5th ed., John Wiley & Sons, Inc., New York, 2000, ISBN 0-471-32013-7 |
Course L1095: Physical and Chemical Basics of Materials Science |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Gregor Vonbun-Feldbauer |
Language | DE |
Cycle | WiSe |
Content |
|
Literature |
Für den Elektromagnetismus:
Für die Atomphysik:
Für die Materialphysik und Elastizität:
|
Module M1802: Engineering Mechanics I (Stereostatics) |
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Courses | ||||||||||||||||
|
Module Responsible | Prof. Benedikt Kriegesmann |
Admission Requirements | None |
Recommended Previous Knowledge |
Solid school knowledge in mathematics and physics. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students can
|
Skills |
The students can
|
Personal Competence | |
Social Competence |
The students can work in groups and support each other to overcome difficulties. |
Autonomy |
Students are capable of determining their own strengths and weaknesses and to organize their time and learning based on those. |
Workload in Hours | Independent Study Time 96, Study Time in Lecture 84 |
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 Civil- and Environmental Engineering: Core Qualification: Compulsory Bioprocess Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Data Science: Specialisation II. Application: Elective Compulsory Electrical Engineering: Core Qualification: Elective Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Orientation Studies: Core Qualification: Elective Compulsory Naval Architecture: Core Qualification: Compulsory Process Engineering: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory |
Course L1001: Engineering Mechanics I (Statics) |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Benedikt Kriegesmann |
Language | DE |
Cycle | WiSe |
Content |
|
Literature |
K. Magnus, H.H. Müller-Slany: Grundlagen der Technischen Mechanik. 7. Auflage, Teubner (2009). D. Gross, W. Hauger, J. Schröder, W. Wall: Technische Mechanik 1. 11. Auflage, Springer (2011). |
Course L1003: Engineering Mechanics I (Statics) |
Typ | Recitation Section (large) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Benedikt Kriegesmann |
Language | DE |
Cycle | WiSe |
Content |
Forces and equilibrium Constraints and reactions Frames Center of mass Friction Internal forces and moments for beams |
Literature |
K. Magnus, H.H. Müller-Slany: Grundlagen der Technischen Mechanik. 7. Auflage, Teubner (2009). D. Gross, W. Hauger, J. Schröder, W. Wall: Technische Mechanik 1. 11. Auflage, Springer (2011). |
Course L1002: Engineering Mechanics I (Statics) |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Benedikt Kriegesmann |
Language | DE |
Cycle | WiSe |
Content |
Forces and equilibrium Constraints and reactions Frames Center of mass Friction Internal forces and moments for beams |
Literature |
K. Magnus, H.H. Müller-Slany: Grundlagen der Technischen Mechanik. 7. Auflage, Teubner (2009). D. Gross, W. Hauger, J. Schröder, W. Wall: Technische Mechanik 1. 11. Auflage, Springer (2011). |
Module M0833: Introduction to Control Systems |
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Courses | ||||||||||||
|
Module Responsible | Prof. Timm Faulwasser |
Admission Requirements | None |
Recommended Previous Knowledge |
Representation of signals and systems in time and frequency domain, Laplace transform |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
|
Skills |
|
Personal Competence | |
Social Competence | Students can work in small groups to jointly solve technical problems, and experimentally validate their controller designs |
Autonomy |
Students can obtain information from provided sources (lecture notes, software documentation, experiment guides) and use it when solving given problems. They can assess their knowledge in weekly on-line tests and thereby control their learning progress. |
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): Core Qualification: Compulsory Bioprocess Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Data Science: Specialisation II. Application: Elective Compulsory Electrical Engineering: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Logistics and Mobility: Specialisation Information Technology: Elective Compulsory Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory Logistics and Mobility: Specialisation Production Management and Processes: Elective Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course Core Studies: Elective Compulsory Process Engineering: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. Information Technology: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. Traffic Planning and Systems: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. Production Management and Processes: Elective Compulsory |
Course L0654: Introduction to Control Systems |
Typ | Lecture |
Hrs/wk | 2 |
CP | 4 |
Workload in Hours | Independent Study Time 92, Study Time in Lecture 28 |
Lecturer | Prof. Timm Faulwasser |
Language | DE |
Cycle | WiSe |
Content |
Signals and systems
Feedback systems
Root locus techniques
Frequency response techniques
Time delay systems
Digital control
Software tools
|
Literature |
|
Course L0655: Introduction to Control Systems |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Timm Faulwasser |
Language | DE |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1807: Machine Learning for Physical Systems |
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Courses | ||||||||||||
|
Module Responsible | Prof. Roland Can Aydin | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
No prior knowledge in machine learning or Python programming is strictly required, although it would be beneficial to have had some degree of experience in one of the standard ML libraries in Python (e.g., PyTorch, Keras, or TF). |
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Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
In this module, students will explore the integration of machine learning techniques with physical systems. The course covers a wide array of topics, demonstrating how machine learning methodologies can be applied not only in non-physical domains but specifically tailored for physical systems. Key areas of study include:
Complementing the lectures, the associated exercise sessions will use various Python libraries such as Sklearn and Pytorch, typically within Jupyter notebooks. These practical sessions are designed to reinforce the concepts discussed in the lectures, with a reciprocal relationship between the theoretical and practical aspects of the course. This course is designed for those looking to understand and apply machine learning in the realm of physical systems, bridging the gap between abstract algorithms and real-world physical phenomena. |
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Skills | The students will be able to competently evaluate suitable machine learning methods for a given problem involving physical systems, understanding the advantages and disadvantages of each approach. They will be able to do so both for standard machine learning tools and methods as well as for specialised models. | ||||||||
Personal Competence | |||||||||
Social Competence |
The students will be able to reason for and against solutions for complex problems involving physical systems and to present their conclusions on how to incorporate their domain knowledge to facilitate the choice, design, training, and validation of an appropriate machine learning algorithm. |
||||||||
Autonomy |
The students will be able to develop solutions for complex problems involving physical systems and to incorporate their domain knowledge to facilitate the choice, design, training, and validation of an appropriate machine learning algorithm. |
||||||||
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 75 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: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Advanced Materials: Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Mechatronics: Elective Compulsory Data Science: Specialisation IV. Special Focus Area: Elective Compulsory Data Science: Specialisation III. Applications: Elective Compulsory Data Science: Specialisation II. Application: Elective Compulsory Engineering Science: Specialisation Advanced Materials: Compulsory Engineering Science: Specialisation Data Science: Elective Compulsory Aeronautics: Core Qualification: Elective Compulsory Mechatronics: Specialisation Robot- and Machine-Systems: Elective Compulsory Mechatronics: Specialisation Dynamic Systems and AI: Elective Compulsory |
Course L2987: Machine Learning for Physical Systems |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Roland Can Aydin |
Language | EN |
Cycle | WiSe |
Content |
Introduction into various approaches and methods for using Machine Learning in conjunction with physical systems. |
Literature | Relevante Literatur basiert vor allem auf wissenschaftlichen Veröffentlichungen (statt Lehrbüchern), die jeweiligen Referenzen werden in der Vorlesung genannt. |
Course L2988: Machine Learning for Physical Systems |
Typ | Project-/problem-based Learning |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Roland Can Aydin |
Language | EN |
Cycle | WiSe |
Content |
The exercise (PBL) demonstrates the methods introduced in the lecture on different example applications, focusing on gaining practical hands-on proficiency. By submitting correctly solved homework assignments, points can be earned for the module examination. Topics include: - Data pre- and postprocessing - Decision-trees and random forests - Convolutional Neural Networks (CNNs) - Physics-informed Neural Networks (PINNs) - Feature selection and feature engineering - Neural architecture search (NAS) and hyperparameter tuning - Constitutive artificial neural networks (CANNs) - Synthetic data - Multimodal and ensemble learning - Optimal experimental design (active learning) - Transformer-based architectures (such as Large Language Models) as applicable for physical systems - Process-structure-properties machine learning pipelines |
Literature | Keine über die in der Vorlesung genannten Referenzen herausgehende Literatur ist notwendig. |
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. |
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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. |
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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. |
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Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 | ||||||||||||
Credit points | 6 | ||||||||||||
Course achievement |
|
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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 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 M1519: Introduction to Electrical Engineering (Technomathematics) |
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Courses | ||||||||||||
|
Module Responsible | Prof. Christian Kautz |
Admission Requirements | None |
Recommended Previous Knowledge | Knowledge in Physics (upper-level secondary school) |
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 | None |
Examination | Subject theoretical and practical work |
Examination duration and scale | online exercises, short presentation, presence exercise, short oral exam |
Assignment for the Following Curricula |
Data Science: Specialisation II. Application: Elective Compulsory Technomathematics: Core Qualification: Compulsory |
Course L2292: Introduction to Electrical Engineering (Technomathematics) |
Typ | Lecture |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Christian Kautz |
Language | DE |
Cycle | SoSe |
Content |
|
Literature |
|
Course L2293: Introduction to Electrical Engineering (Technomathematics) |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Christian Kautz |
Language | DE |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1004: Logistics Management |
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Courses | ||||||||||||
|
Module Responsible | Dr. Meike Schröder | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
Introduction to Business and Management |
||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
Students will be able
|
||||||||
Skills |
Based on the acquired knowledge students are capable of
|
||||||||
Personal Competence | |||||||||
Social Competence |
Students can
|
||||||||
Autonomy |
Students are able to - assess their own state of learning in specific terms and to define further work steps on this basis guided by teachers. |
||||||||
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 120 min | ||||||||
Assignment for the Following Curricula |
Data Science: Specialisation II. Application: Elective Compulsory Logistics and Mobility: Core Qualification: Compulsory Orientation Studies: Core Qualification: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory |
Course L1222: Introduction into Production Logistics |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Dr. Yong Lee |
Language | DE |
Cycle | SoSe |
Content |
In the era of time-competition production and logistics need to be considered as a combined strategic competitive advantage. "Introduction in to production logistics" gives an overview over the different disciplinces of production logistics: - Development from cost-, quality to time-competitiion, - fundamentals of production and logistics, - phase-oriented and functional subsystems of production logistics, - planning and steering, - analysis and optimization (focus: Lean Management), - production logistics controlling and supply-chain management in production network Theory is complented by case studies and guest presentations. |
Literature |
|
Course L1221: Logistics Economics |
Typ | Project-/problem-based Learning |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Dr. Meike Schröder |
Language | DE |
Cycle | SoSe |
Content |
|
Literature |
|
Module M0767: Aeronautical Systems |
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Courses | ||||||||||||||||||||
|
Module Responsible | Prof. Frank Thielecke |
Admission Requirements | None |
Recommended Previous Knowledge | Basics of mathematics, mechanics and thermodynamics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge | Students get a basic understanding of the structure and design of an aircraft, as well as an overview of the systems inside an aircraft. In addition, a basic knowledge of the relationchips, the key parameters, roles and ways of working in different subsystems in the air transport is acquired. |
Skills | Due to the learned cross-system thinking students can gain a deeper understanding of different system concepts and their technical system implementation. In addition, they can apply the learned methods for the design and assessment of subsystems of the air transportation system in the context of the overall system. |
Personal Competence | |
Social Competence | Students are made aware of interdisciplinary communication in groups. |
Autonomy | Students are able to independently analyze different system concepts and their technical implementation as well as to think system oriented. |
Workload in Hours | Independent Study Time 96, Study Time in Lecture 84 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 150 min |
Assignment for the Following Curricula |
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Aircraft Systems Engineering: Compulsory Data Science: Specialisation II. Application: Elective Compulsory Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory Mechanical Engineering: Specialisation Aircraft Systems Engineering: Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. Traffic Planning and Systems: Elective Compulsory |
Course L0741: Fundamentals of Aircraft Systems |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Frank Thielecke |
Language | DE |
Cycle | SoSe |
Content |
|
Literature |
|
Course L0742: Fundamentals of Aircraft Systems |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Frank Thielecke |
Language | DE |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L0591: Air Transportation Systems |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Volker Gollnick |
Language | DE |
Cycle | SoSe |
Content |
|
Literature |
|
Course L0816: Air Transportation Systems |
Typ | Recitation Section (large) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Volker Gollnick |
Language | DE |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1277: MED I: Introduction to Anatomy |
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Courses | ||||||||
|
Module Responsible | Prof. Michael Morlock |
Admission Requirements | None |
Recommended Previous Knowledge |
Students can listen to the lectures without any prior knowledge. Basic school knowledge of biology, chemistry / biochemistry, physics and Latin can be useful. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The lectures are about microscopic anatomy, describing the microscopic structure of tissues and organs, and about macroscopic anatomy which is about organs and organ systems. The lectures also contain an introduction to cell biology, human development and to the central nervous system. The fundamentals of radiologic imaging are described as well, using projectional x-ray and cross-sectional images. The Latin terms are introduced. |
Skills |
At the end of the lecture series the students are able to describe the microscopic as well as the macroscopic assembly and functions of the human body. The Latin terms are the prerequisite to understand medical literature. This knowledge is needed to understand und further develop medical devices. These insights in human anatomy are the fundamentals to explain the role of structure and function for the development of common diseases and their impact on the human body. |
Personal Competence | |
Social Competence |
The students can participate in current discussions in biomedical research and medicine on a professional level. The Latin terms are prerequisite for communication with physicians on a professional level. |
Autonomy |
The lectures are an introduction to the basics of anatomy and should encourage students to improve their knowledge by themselves. Advice is given as to which further literature is suitable for this purpose. Likewise, the lecture series encourages students to recognize and think critically about biomedical problems. |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Credit points | 3 |
Course achievement | None |
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 General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory Data Science: Specialisation II. Application: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Engineering Science: Specialisation Biomedical Engineering: Compulsory General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory Mechanical Engineering: Specialisation Biomechanics: Compulsory Mechatronics: Specialisation Medical Engineering: Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory |
Course L0384: Introduction to Anatomy |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Dr. Thorsten Frenzel |
Language | DE |
Cycle | SoSe |
Content |
General Anatomy 1st week: The Eucaryote Cell 2nd week: The Tissues 3rd week: Cell Cycle, Basics in Development 4th week: Musculoskeletal System 5th week: Cardiovascular System 6th week: Respiratory System 7th week: Genito-urinary System 8th week: Immune system 9th week: Digestive System I 10th week: Digestive System II 11th week: Endocrine System 12th week: Nervous System 13th week: Exam |
Literature |
Adolf Faller/Michael Schünke, Der Körper des Menschen, 18. Auflage, Thieme Verlag Stuttgart, 2020, 704 Seiten, ISBN 978-3-13-243820-0 |
Module M1278: MED I: Introduction to Radiology and Radiation Therapy |
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Courses | ||||||||
|
Module Responsible | Prof. Michael Morlock |
Admission Requirements | None |
Recommended Previous Knowledge | None |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Therapy
The students can distinguish different types of currently used equipment with respect to its use in radiation therapy. The students can explain treatment plans used in radiation therapy in interdisciplinary contexts (e.g. surgery, internal medicine). The students can describe the patients' passage from their initial admittance through to follow-up care. Diagnostics The students can illustrate the technical base concepts of projection radiography, including angiography and mammography, as well as sectional imaging techniques (CT, MRT, US). The students can explain the diagnostic as well as therapeutic use of imaging techniques, as well as the technical basis for those techniques. The students can choose the right treatment method depending on the patient's clinical history and needs. The student can explain the influence of technical errors on the imaging techniques. The student can draw the right conclusions based on the images' diagnostic findings or the error protocol. |
Skills |
Therapy
The students can distinguish curative and palliative situations and motivate why they came to that conclusion. The students can develop adequate therapy concepts and relate it to the radiation biological aspects. The students can use the therapeutic principle (effects vs adverse effects) The students can distinguish different kinds of radiation, can choose the best one depending on the situation (location of the tumor) and choose the energy needed in that situation (irradiation planning). The student can assess what an individual psychosocial service should look like (e.g. follow-up treatment, sports, social help groups, self-help groups, social services, psycho-oncology). Diagnostics The students can suggest solutions for repairs of imaging instrumentation after having done error analyses. The students can classify results of imaging techniques according to different groups of diseases based on their knowledge of anatomy, pathology and pathophysiology. |
Personal Competence | |
Social Competence |
The
students can assess the special social situation of tumor patients and interact
with them in a professional way.
The students are aware of the special, often fear-dominated behavior of sick people caused by diagnostic and therapeutic measures and can meet them appropriately. |
Autonomy |
The
students can apply their new knowledge and skills to a concrete therapy case.
The students can introduce younger students to the clinical daily routine. The students are able to access anatomical knowledge by themselves, can participate competently in conversations on the topic and acquire the relevant knowledge themselves. |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Credit points | 3 |
Course achievement | None |
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 General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory Data Science: Specialisation II. Application: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Engineering Science: Specialisation Biomedical Engineering: Compulsory General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory Mechanical Engineering: Specialisation Biomechanics: Compulsory Mechatronics: Specialisation Medical Engineering: Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory |
Course L0383: Introduction to Radiology and Radiation Therapy |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Dr. Thorsten Frenzel |
Language | DE |
Cycle | SoSe |
Content |
The students will be given an understanding of the technological possibilities in the field of medical imaging, interventional radiology and radiation therapy/radiation oncology. It is assumed, that students in the beginning of the course have heard the word “X-ray” at best. It will be distinguished between the two arms of diagnostic (Prof. Dr. med. Thomas Vestring) and therapeutic (Prof. Dr. med. Ulrich Carl) use of X-rays. Both arms depend on special big units, which determine a predefined sequence in their respective departments |
Literature |
|
Thesis
Module M-001: Bachelor Thesis |
||||
Courses | ||||
|
Module Responsible | Professoren der TUHH |
Admission Requirements |
|
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 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): Thesis: Compulsory General Engineering Science (German program, 7 semester): Thesis: Compulsory Civil- and Environmental Engineering: Thesis: Compulsory Bioprocess 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 General Engineering Science (English program): Thesis: Compulsory General Engineering Science (English program, 7 semester): Thesis: Compulsory Green Technologies: Energy, Water, Climate: Thesis: Compulsory Computer Science in Engineering: Thesis: Compulsory Logistics and Mobility: Thesis: Compulsory Mechanical Engineering: Thesis: Compulsory Mechatronics: Thesis: Compulsory Naval Architecture: Thesis: Compulsory Technomathematics: Thesis: Compulsory Teilstudiengang Lehramt Metalltechnik: Thesis: Compulsory Process Engineering: Thesis: Compulsory Engineering and Management - Major in Logistics and Mobility: Thesis: Compulsory |