Module summary
Module name:
Internal number:
Coordinator:
Extent:
Semester:
Pre-requisites with regard to content: none
Pre-requisites according to the examination regulations:
none
Competencies:

The student should be able to lay his emphasis on individual interests.

Assessment:
Individual exams
Course: Affective Computing
Internal number: I W924 Type/mode: Lecture
Lecturers:
Prof. Thomas Hinz
M.Sc. Bernd Dudzik
Language of instruction:
German
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours presence, 30 hours self-contained work) Assessment: Homework 1 Semester (graded)
Content:

Emotional expressions are important signals for people to make sense of situations, actions and relationships in their social interactions with each other. Is the empowerment of technological systrms with the capacity to also sense and express emotions able to improve their users’ interactions with them? This question is the driving force behind the field of Affective Computing.

The students know different theories of emotions, contrast them with each other and debate them. They apply the acquired knowledge by addressing problems from within the primary areas of application for Affective Computing through the development of  prototypical interactive systems that are capable of sensing or expressing emotions.

Recommended reading:

Lecture notes, case studies.

Comments: This course is either not offered in Summer semester 2021, or the date is not yet fixed in case of a block course.
Course: App-Programming
Internal number: I W912 Type/mode: Lecture
Lecturer:
M.Sc. Adrian Wörle
Language of instruction:
German
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours presence, 30 hours self-contained work) Assessment: Written Exam 90 Min. (graded)
Content:

The lecture teaches the construction of mobile media applications. The main concepts are discussed using the Android platform. In a first partt, the basic technologies and limitations of mobile devices are shown. The second part examins different development strategies like native applications, device independend abstractions and web applications. A main part of the lecture is the integration of different media types into mobile applications and the constraints the developer has to keep in mind.

Recommended reading:

will be announced

Comments:

Leacture with exercise

Course: Big Data Engineering
Internal number: I W926 Type/mode: Lecture
Lecturer:
Prof. Dr. Christian Zirpins
Language of instruction:
German
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours presence, 30 hours self-contained work) Assessment: Written Exam 90 Min. (graded)
Content:

The lecture Big Data Engineering addresses the systematic construction of data-intensive systems. Generic architectural approaches are introduced in order to design robust, performant and scalable data systems for various applications. For different architectural areas various kinds of data storage and processing models are discussed. Topics include, among others, distributed file systems, serialization, batch and stream processing with MapReduce and other programming models, queuing mechanisms and NoSQL databases. These are both conceptually described as well as implemented by means of exemplary tools and techniques. The focus is on established industry standards such as Apache Thrift, Hadoop, Kafka, Cassandra, Storm. These are illustrated by means of an exemplary Web Analytics application.

 

During the course students acquire, among others, the following abilities:

  • They evaluate different approaches of data systems for given application problems with specific requirements.
  • They describe structure and function of specific architectural approaches for Big Data systems.
  • They categorize tools and techniques for Big Data systems and utilize them professionally.
  • They design architecture and data models as well as processing logic and queries for given Big Data applications and implement these based on specific open source tools and techniques.
Recommended reading:
  • Nathan Marz, James Warren, "Big Data: Principles and best practices of scalable realtime data systems", Manning, 2015, ISBN: 1-617290-34-3
  • Martin Kleppmann, "Designing Data-Intensive Applications", O'Reilly, 2014 (Early Release), ISBN: 978-1-4493-7332-0
  • Tom White, "Hadoop: the definitive guide: storage and analysis at internet scale", 4. ed., O'Reilly, 2015, ISBN: 978-1-491-90163-2    
  • Michael Frampton, "Big Data Made Easy: A Working Guide to the Complete Hadoop Toolset", Apress, 2015, ISBN: 978-148-420-094-0
  • Vivek Mishra, "Beginning Apache Cassandra Development", Apress, 2014, ISBN: 978-148-420-142-8
  • Additional literature will be announced during the lecture
Comments:

Independent work relates to the preparation and followup of lectures, laboratory exercises and exam preparation.

This course is either not offered in Summer semester 2021, or the date is not yet fixed in case of a block course.
Course: Business Intelligence
Internal number: I W179 Type/mode: Lecture
Lecturer:
Prof. Dr. Uwe Haneke
Language of instruction:
German
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours presence, 30 hours self-contained work) Assessment: Written Exam 90 Min. (graded)
Content:
Students can apply the theoretical concepts developed during the lecture by working on case studies and the possibility to evaluate different software tools.
  • Introduction and business-management background
  • The concept of data warehousing
  • Business Analytics and Balanced Scorecard (BSC)
  • CRM and Data Mining
  • Trends in Business Intelligence-Case studies
Recommended reading:
PowerPoint slides, exercise-sheets, continuative information on the web-site and in the ILIAS-eLearning-system, access to different BI-tools via VMware server and the SAP competence center. Bauer, A., Günzel, H. (Hrsg.) (2004): Data Warehouse-Systeme - Architektur, Entwicklung, Anwendung. dpunkt.Verlag, Heidelberg.
Comments: Lecture combined with exercise sessions and case studiesThis course is either not offered in Summer semester 2021, or the date is not yet fixed in case of a block course.
Course: Cloud Computing
Internal number: I W913 Type/mode: Lecture
Lecturers:
Dipl. Inform. (FH) Georg Magschok
Dipl. Inform. (FH) Michael Fischer
Language of instruction:
German
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours presence, 30 hours self-contained work) Assessment: Written Exam 90 Min. (graded)
Content:

The buzzword "Cloud" represents a variety of interesting technologies which gained importance in the life of a computer science professional. Those are being collected, examined, explained and understood during the course. Primary objective is usefulness for the student, regardless of whether he acts as a cloud user, developer, administrator or even entrepreneur. Understand the broad meaning of "Cloud Computing" from a variety of perspectives: Definition, use cases, technology basics, key players, APIs, scaling, redundancy …

Recommended reading:

Powerpoint slides

Comments:
Course: Computer Vision
Internal number: I W772 Type/mode: Lecture
Lecturer:
Prof. Dr.-Ing. Astrid Laubenheimer
Language of instruction:
German
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours presence, 30 hours self-contained work) Assessment: Written Exam 60 Min. (graded)
Content:
Recommended reading:
Comments:
Course: Computer Vision Laboratory
Internal number: I W773 Type/mode: Laboratory
Lecturer:
Prof. Dr.-Ing. Astrid Laubenheimer
Language of instruction:
German
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours presence, 30 hours self-contained work) Assessment: Laboratory Work 1 Semester (graded)
Content:
Application: Prior registration or agreement with a lecturer required
Recommended reading:
Comments:
Course: Digital Transformation & digital marketing
Internal number: I W929 Type/mode: Lecture
Lecturers:
Marc Steinmetz
Prof. Thomas Hinz
Language of instruction:
German
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours presence, 30 hours self-contained work); Assessment: Written Exam 90 Min. (graded)
Content:
Recommended reading:
Comments:
Course: Game Programming
Internal number: I W620 Type/mode: Lecture
Lecturer:
Prof. Dr. Peter Henning
Language of instruction:
English
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours presence, 30 hours self-contained work) Assessment: Written Exam 90 Min. (graded)
Content:
Recommended reading:
Comments:
Course: New Lecture
Internal number: I W156 Type/mode: Lecture
Lecturer:
Prof. Dr. Martin Sulzmann
Language of instruction:
English
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours presence, 30 hours self-contained work); Assessment: Written Exam 90 Min. (graded)
Content:
Recommended reading:
Comments: This course is either not offered in Summer semester 2021, or the date is not yet fixed in case of a block course.
Course: New Lecture
Internal number: I W501 Type/mode: Lecture
Lecturer:
Prof. Dr. Frank Schaefer
Language of instruction:
German
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours of presence, 30 hours self-contained work); Assessment: Written/verbal Exam 90/20 Min. (graded)
Content:
Recommended reading:
Comments:
Course: New Lecture
Internal number: I W502 Type/mode: Lecture
Lecturer:
Prof. Dr. Thomas Morgenstern
Language of instruction:
German
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours of presence, 30 hours self-contained work); Assessment: Presentation 20 Min. (graded)
Content:
Recommended reading:
Comments:
Course: Parallel Systems
Internal number: I W391 Type/mode: Lecture
Lecturer:
Prof. Dr. Christian Langen
Language of instruction:
German
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours of presence, 30 hours self-contained work); Assessment: Written Exam 90 Min. (graded)
Content:
Recommended reading:
Comments:
Course: Video
Internal number: I W925 Type/mode: Lecture
Lecturers:
Prof. Thomas Hinz
Marc Steinmetz
Language of instruction:
German
Credits (ECTS): 2 Contact hours: 2
Workload: 60 hours (30 hours presence, 30 hours self-contained work) Assessment: Homework 1 Semester (graded)
Content:
Recommended reading:
Comments: