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Studium

Unseren Studierenden vermitteln wir praxisorientiertes Sachwissen und überfachliche Kompetenzen.

Big Data Algorithms and Systems

Lecturer	Prof. Dr. Siebert Andreas
Type of course	Lecture / Tutorials
ECTS credits	5
Semester	Summer semester
Admission Requirements	Algorithms and data structures; programming knowledge
Objectives	Become familiar with basic algorithms in the field of big data and be able to apply them Become familiar with systems that are used to process very large volumes of – in particular – unstructured data and be able to assess when it is appropriate to use them

Internet of Things

Internet of Things

Lecturer	Prof. Dr. Abdelmajid Khelil, Dominik Obermaier
Type of course	Lecture / Tutorials
ECTS credits	5
Semester	Summer semester
Admission Requirements
Objectives	The syllabi will be communicated soon

Cloud Computing

Cloud Computing

Lecturer	Prof. Dr. Markus Mock
Type of course	Lecture
ECTS credits	5
Semester	Summer Term
Admission Requirements	Programming experience and a course in algorithms and data structures
Objectives	Become familiar with the importance of resource management and the concept of elasticity in the cloud Learn about strategies for synchronizing distributed data sources Explain the advantages and disadvantages of virtualized infrastructures Become ready to launch an application that uses cloud infrastructure for processing or data storage in the cloud Ability to structure an application appropriately between client and cloud rescources Learn about important computing paradigms for highly distributed processing such as Mapreduce

Innovation Lab (IoT-Project)

Lecturer	Prof. Dr. Abdelmajid Khelil
ECTS	5
Semester	Summer
Admission Requirements	Experience in Software Enigneering and Programming
Objectives	Students will identify real-world problems and recognise the problems of creating complex solutions using a wide variety of IoT platforms. They are in a position to analyse the environment of the problem and are able to discuss these in advance in cooperation with companies. Knowledge of design thinking, agile project management and the independent implementation of projects is acquired in teamwork. They are able to apply interdisciplinary knowledge, integrate the problem solver into the project in an agile manner and to present the results of their work.
Teaching Content	The cooperating companies offer the students real problems from the most important IoT domains, such as Smart Agriculture, Smart Building, Smart Energy, Smart Production, eHealth, etc. The problem is described in detail using defined application cases. In addition, the aspects of IoT Cloud and IoT Security are also examined. The students are supervised by the lecturer and the coach of the innovation lab.

Introduction to Artificial Intelligence

Lecturer	Eduard Kromer
ECTS	5
Semester	Summer
Admission Requirements	Experience in Java-Programming
Objectives	Students will gain an overview of the methods of artificial intelligence. They will be able to apply automatic reasoning for the design and implementation of expert systems. They know which kind of problems can be solved with automatic planners and know the advantages and disadvantages of specific planning algorithms.
Course Content	Definition and overview of Artificial Intelligence Pradicate logic Inference engines and knowledge-based behavior Planning algorithms Modeling of planning problems with jBoss Drools

Study Project

Lecturer	Lecturers of CS
ECTS	5
Semester	Summer
Admission Requirements	Experience in Programming and Software Engineeering
Objectives	The students know the problems of creating complex systems. They can apply the basics of scientific work and know how to independently carry out projects appropriate to the degree programme. They have learned to work in a team and have acquired knowledge in estimating the scope of projects as well as in the management and supervision of projects. They are able to apply interdisciplinary knowledge and present work results.
Course Content	The teachers of the Faculty of Computer Science offer the students a choice of project topics with a short description. Teams of students can propose a project themselves, for this you must find a supervising lecturer. The students are regularly supervised professionally by the issuing lecturer.

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