After successful completion of the course, students are able to understand:

(1) The modeling of quantum bits and quantum registers, in particular the visualization of qubits on the Bloch sphere, including the required foundations from quantum mechanics and the theory of Hilbert spaces.
(2) The elementary quantum algorithms of Deutsch, Deutsch-Josza, and Simon, their analyses, and the principles of quantum computing.
(3) The building blocks of quantum algorithms including the Pauli, Hadamard, rotation, and controlled operators and the analysis of such algorithms.
(4) The polynomial time quantum algorithms of Shor for factoring integers and computing discrete logarithms and their relevance for cryptography.
(5) Grover’s search algorithms and the related counting algorithms.
(6) Basics of quantum computability and complexity theory, including the construction of a universal set of quantum gates and quantum complexity classes.

After successful completion of the course, students are able to

• explain what computer science is about
• present structures and processes of a university
• apply learning methods and how to organize themselves to make progress while studying computer science or business informatics
• select and apply strategies, methods, and tools to deal with different forms of thinking and problem-solving
• search for knowledge
• deal with possibilities to decide for a study program
• orient regarding their own carriers and future job possibilities
• get to learn other students and exchange with them

This course provides an introduction to computer science as a field of study and as a field of work. The LVA should arouse interest in further studies and enable students to better classify the contents presented in further studies.

After successful completion of the course, students are able to

• desribe the problem tackled in their master thesis and its relevance
• select an appropriate resarch method to tackle the problem
• define and present a approriate approach to solve the problem
• evaluate the approach with respect to the problem definition
• present and defend the proposal as well as the results of the master thesis

After successful completion of the course, students are able to submit the Master thesis proposal, which motivates and defines the topic of their thesis, introduces the planned research questions and aims of the thesis, describes the methodology, presents the state of the art, and places their thesis in the context of the Master program Software Engineering and Internet Computing.

After successful completion of the course, students are able to

• desribe the problem tackled in their master thesis and its relevance
• select an appropriate resarch method to tackle the problem
• define and present a approriate approach to solve the problem
• evaluate the approach with respect to the problem definition
• present and defend the proposal as well as the results of the master thesis

After successful completion of the course, students are able to

• describe important concepts of a modern programming language
• convert content of natural language programming tasks into executable small programs
• apply practices and tools during the implementation of small programms
• implement and analyze selected algorithms

After successful completion of the course, students are able to successfully present and defend their own scientific work. In the course of the seminar, topics of the individual students are presented and discussed in order to support the participants in their research projects as well as to give them an opportunity to network and exchange ideas.

After successful completion of the course, students are able to understand penetrate scientific literature in depth, derive open scientific questions from it, and check their implementation potential.

After successful completion of the course, students are able to understand, analyze, develop, and use model-driven software engineering techniques.

This module deals with model-driven approaches to software engineering. It combines techniques, methods, and tools from language engineering and model engineering.

Fachkompetenzen: Fundamental concepts and techniques of model-driven software engineering including in particular the development of domain-specific languages (metamodeling), concrete syntaxes, model transformators, and code generators; and the application thereof.

After successful completion of the module, students are able to

• apply model-driven software development or information system development to practical tasks,
• develop modeling languages and the required tool environment based on OMG’s meta-modeling stack,
• evaluate transformation languages and use them for vertical, horizontal and temporal model transformation, 
• evaluate and use textual and graphical modeling languages, 
• evaluate language architectures, i.a. using the example of UML, 
• use extension mechanisms of languages, i.a. UML profiles,
• use constraint languages, i.a. OCL to specify additional constraints on modeling languages,
• implement code generators, and 
• solve tasks of model management, i.a. model evolution, model versioning and model storage.

Überfachliche Kompetenzen: Students acquire the ability to explain methods for model-driven software engineering and design domain-specific model-driven software engineering solutions. Furthermore, students will be able to identify, articulate, and discuss issues
concerning ethics, gender, and diversity in the context of the module’s content.

After successful completion of the course, students are able to:

• to find and discuss the relevant literature for a given topic
• to conduct a project which meets scientific requirements, by applying the knowledge and capabilities gained in the bachelor studies in a large-scale problem setting
• to describe the task, the methodology, the technical approach (if applicable), the setting, and the results of the project in a written, scientific thesis