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Studium

Unseren Studierenden vermitteln wir praxisorientiertes Sachwissen und überfachliche Kompetenzen.

Big Data Algorithms and Systems

Lecturer	Prof. Dr. Andreas Siebert
Type of course	Lecture / Tutorials
ECTS credits	5
Semester	Summer Semester
Admission Requirements	Algorithms and data structures Programming knowledge B2 Level in English
Format	On Campus
Objectives	Throughout the course, students: 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
Type of course	Lecture / Tutorials
ECTS credits	5
Semester	Summer Semester
Module Number	IB674
Admission Requirements	B2 Level in English
Format	On Campus
Objectives	After successful completion of this course, students are able to: Identify real-world problems and recognize the core issue of creating complex solutions using a wide range of IoT platforms. Analyze the context of a given problem and discuss these in advance in cooperation with companies. Acquire knowledge about Design Thinking, agile project management and independent execution of projects in teamwork. Apply interdisciplinary knowledge, integrate the problem poser into the project in an agile manner and present the results of their work.

Cloud Computing

Cloud Computing

Lecturer	Prof. Dr. Markus Mock
Type of course	Lecture
ECTS credits	5
Semester	Summer Semester
Admission Requirements	Programming experience and a course in algorithms and data structures B2 Level in English
Format	On Campus
Objectives	Throughout the course, students: 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 Develop the 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 Semester
Admission Requirements	Experience in Software Enigneering and Programming B2 Level in English
Format	On Campus
Objectives	Throughout the course, students: 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. Acquire 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.

Study Project

Study Project

Lecturer	Lecturers of CS
ECTS	5
Semester	Summer Semester
Admission Requirements	Experience in Programming and Software Engineeering B2 Level in English
Format	On Campus
Objectives	After successful completion of this course, students: Know the problems of creating complex systems Can apply the basics of scientific work and know how to independently carry out projects appropriate to the degree programme 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. Are able to apply interdisciplinary knowledge and present work results.
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.

Machine Learning in the Cloud

Machine Learning in the Cloud

Lecturer	Prof. Dr. Markus Mock
Type of course	Lecture / Accompanying Internship
ECTS credits	5
Semester	Summer Semester
Admission Requirements	B2 Level in English
Previous Knowledge	Knowledge in Cloud computing basics e.g. through IB768, programming knowledge, Python is an advantage.
Format	On Campus
Objectives	After successful completion of this course, students are able to: Implement Machine Learning in the cloud and be familiar with various machine learning methods. Implement the learned methods in a cloud environment and solve practical ML problems. Select appropriate cloud infrastructure and services for the given problems and be familiar with the practical use of standard tools for this purpose.