Big Data Algorithms and Systems

Lecturer

Prof. Dr. Andreas Siebert

Type of courseLecture / Tutorials
ECTS credits5
SemesterSummer Semester
Module NumberIB760
Admission Requirements

Algorithms and data structures

Programming knowledge

B2 Level in English

FormatOn Campus
Objectives

The students will be able to understand, to analyze, and to apply algorithms that are taylored to large data sets.

In particular, online algorithms, novel hashing algorithms, point algorithms, and string algorithms are covered.

Bachelor Thesis

Lecturer

 

Type of courseBachelor Thesis
ECTS credits12
SemesterWinter Semester
Module NumberIB720/ KI710
Admission Requirements

B2 Level in English

FormatOn Campus
Objectives

 

Internet of Things

Lecturer

Prof. Dr. Abdelmajid Khelil

Type of courseLecture / Tutorials
ECTS credits5
SemesterSummer Semester
Module NumberIB674
Admission RequirementsB2 Level in English
FormatOn 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.
 

Innovation Lab (IoT-Project)

LecturerProf. Dr. Abdelmajid Khelil
ECTS5
SemesterWinter and Summer Semester
Module NumberIB765
Admission Requirements

Experience in Software Enigneering and Programming

B2 Level in English

FormatOn 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

LecturerLecturers of CS
ECTS5
SemesterWinter Semester, Summer Semester
Module NumberIB351
Admission Requirements

Experience in Programming and Software Engineeering

B2 Level in English

FormatOn 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.
ContentThe 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.

Foundations of Scientific Work

LecturerProf. Dr. Markus Böhm
ECTS2 (Per Semester)
SemesterWinter and Summer Semester
Module NumberWIF290
Admission Requirements--
FormatOn Campus
Objectives

Students are motivated to work scientifically and will be able to acquire subject-specific knowledge from the scientific literature and to prepare this knowledge for specific target groups.


The course covers four areas:

1. Basics of scientific work

Students understand the necessity of a scientific approach to problems and are able to understand the basic concepts of scientific work. (e.g. research questions, argumentation logic, writing style, citation)

2. Research methods

Students are able to apply essential research methods commonly used in business informatics and to assess the basic applicability of these methods for a given problem. In addition, they understand the basics of design-oriented research (Design Science). Furthermore, they are able to conduct a systematic literature study on their own.

3. Handling of scientific texts

Students can describe the structure of scientific texts, apply reading strategies and assess their basic scientific quality. Furthermore, they can compile, evaluate and compare the core statements of different scientific texts.

4. Presentation and discussion

In the area of presentation and discussion, students understand the essential elements of effective presentations and are able to apply them to a lecture. In addition, they are able to apply argumentation strategies for professional discussions and methods for effective discussion moderation.


Implicitly, this course promotes the English language level of the students to the level B2.2/C1.1 of the CEFR. Through intensive literature work with English-language scientific texts and their presentation/discussion, they have the ability to understand the main content of complex texts on concrete and abstract topics as well as to participate in specialist discussions in the field of business information systems.

Cloud Computing

Lecturer

Prof. Dr. Markus Mock

Type of courseLecture
ECTS credits5
SemesterSummer Semester (2025)
Module NumberIB768/KI620
Admission Requirements

B2 Level in English

FormatOn Campus
Objectives

Machine Learning in the Cloud

Lecturer

Prof. Dr. Markus Mock

Type of courseLecture
ECTS credits5
SemesterWinter Semester (2024/2025)
Module NumberKI720
Admission Requirements

B2 Level in English

FormatOn Campus
Objectives

 

Machine Learning II

Lecturer

Prof. Dr. Sandra Eisenreich

Type of courseLecture
ECTS credits8
SemesterSummer Semester (2025)
Module NumberKI440
Admission Requirements

B2 Level in English

FormatOn Campus
Objectives

 

Reinforcement Learning

Lecturer

Prof. Dr. Eduard Kromer

Type of courseLecture
ECTS credits5
SemesterWinter Semester (2024/2025)
Module NumberKI740
Admission Requirements

B2 Level in English

FormatOn Campus
Objectives

3D Game Engines

Lecturer

Prof. Dr. Christopher Auer

Type of courseLecture
ECTS credits5
SemesterSummer Semester (2025)
Module NumberKI630
Admission Requirements

B2 Level in English

FormatOn Campus
Objectives