Module Handbook

  • Dynamischer Default-Fachbereich geändert auf SO

Notes on the module handbook of the department Social Sciences

Die hier dargestellten Studiengang-, Modul- und Kursdaten des Fachbereichs Sozialwissenschaften [SO] befinden sich noch in Entwicklung und sind nicht offiziell.

Die offiziellen Modulhandbücher finden Sie unter .

Module SO-08-2611-M-6

Computation -. Specialized Seminars (M, 12.0 LP)

Module Identification

Module Number Module Name CP (Effort)
SO-08-2611-M-6 Computation -. Specialized Seminars 12.0 CP (360 h)


CP, Effort 12.0 CP = 360 h
Position of the semester 2 Sem. from WiSe/SuSe
Level [6] Master (General)
Language [EN] English
Module Manager
Area of study [INF-VIS] Visualisation and Scientific Computing
Reference course of study [SO-88.A95-SG] M.Sc. Cognitive Science
Livecycle-State [NORM] Active


Type/SWS Course Number Title Choice in
Presence-Time /
SL SL is
required for exa.
PL CP Sem.
2V+1U INF-16-52-K-5
Human Computer Interaction
WP 42 h 78 h
- no 4.0 WiSe
3V+1U INF-61-53-K-6
Biologically Motivated Robots
WP 56 h 64 h
- no 4.0 WiSe
2S INF-16-71-K-7
Visualisation and HCI (Seminar)
WP 28 h 92 h
- no 4.0 WiSe
2V+1U EIT-ISE-110-K-7
WP 42 h 78 h
- no 4.0 WiSe
2V+2L EIT-ISE-112-K-7
Sensor Signal Processing
WP 56 h 94 h
- no 5.0 WiSe
2S SO-04-26.2000-K-7
Man as machine - machines as man
WP 28 h 92 h
- no 4.0 WiSe
  • About [INF-16-52-K-5]: Title: "Human Computer Interaction"; Presence-Time: 42 h; Self-Study: 78 h
  • About [INF-16-52-K-5]: The study achievement "[U-Schein] proof of successful participation in the exercise classes (ungraded)" must be obtained.
  • About [INF-61-53-K-6]: Title: "Biologically Motivated Robots"; Presence-Time: 56 h; Self-Study: 64 h
  • About [INF-61-53-K-6]: The study achievement "[AUSARB_P] written elaboration and presentation" must be obtained.
  • About [INF-16-71-K-7]: Title: "Visualisation and HCI (Seminar)"; Presence-Time: 28 h; Self-Study: 92 h
  • About [INF-16-71-K-7]: The study achievement "[AUSARB_P] written elaboration and presentation" must be obtained.
  • About [EIT-ISE-110-K-7]: Title: "Neurocomputing"; Presence-Time: 42 h; Self-Study: 78 h
  • About [EIT-ISE-110-K-7]: The study achievement "[PRAES] presentation" must be obtained.
  • About [EIT-ISE-112-K-7]: Title: "Sensor Signal Processing"; Presence-Time: 56 h; Self-Study: 94 h
  • About [EIT-ISE-112-K-7]: The study achievement "[PRAES] presentation" must be obtained.
  • About [SO-04-26.2000-K-7]: Title: "Man as machine - machines as man"; Presence-Time: 28 h; Self-Study: 92 h
  • About [SO-04-26.2000-K-7]: The study achievement "[PRAES] presentation" must be obtained.

Examination achievement PL1

  • Form of examination: oral examination (15-30 Min.)
  • Examination Frequency: each semester
  • Examination number: 60100 ("Advanced Module 5: Computation - Specialized Seminars")

Evaluation of grades

The grade of the module examination is also the module grade.


The course introduces students to the theory and applications of human computer interaction (HCI). Students should achieve an understanding of human perception and psychology related to HCI, as well as learn about concepts and methods of interactive systems. The course builds on theoretical principles and numerous examples from research and practice.

Thematic priorities are:

  • Goals and fundamentals of human computer interaction
  • Human perception and cognition: fundamentals, preattentive perception
  • Relations between psychology and interaction design
  • Hardware used for man-machine interaction (I/O-devices)
  • Human-centered approaches
  • Usability: definitions and standards, measuring usability
  • User Analysis & User Modeling, Task Analysis & Task Modeling
  • Interaction models, interaction styles
  • Scalability
  • Interaction metaphors: basics, examples
  • Evaluation: methods, techniques, basics

In the exercises, the lecture topics will be deepened and expanded. For this, the students work through current, lecture-related publications of the most important HCI conferences (e.g., CHI, UIST, IUI, Interact). Second, the prototypical implementation (from paper mock-up to concrete implementation, e.g., in Flash or HTML5) and evaluation of user interfaces is practiced in small groups.

The lecture Biologically motivated robots (BioBots) deals with systems whose mechanical construction, sensor concepts and control methods have been inspired by nature. The following themes will be taught:
  • Status of research and requirements for the development of BioBots
  • Sensor systems, sensor fusion and driving concepts
  • Adaptive control (neural networks, fuzzy-control, Reinforcement-Learning, genetic algorithms and neuro-oszillators)
  • Behaviour based control architectures
  • Application for BioBots
Selected topics from visualization, e.g.:
  • VR/AR
  • Information Visualization
  • Scientific Visualization
  • Adaptive/mobile Visualization
  • Visualization of medical and biological data
  • Introduction to the field of innovative computer architectures and systems for the technical implementation of biological information processing principles
  • Presentation of diverse aims and solution concepts: Hardware for technical cognition systems, biological-technical interfaces, simulation and verification of models of biological evidence
  • Rehearsal of relevant and commonly applied neural algorithms, including deep networks/deep-learning and analysis of computational requirements and operators
  • Extension from amplitude-coded to spike-coded representation and processing
  • Presentation and effect of potential simplification options for the regarded algorithms
  • Basics of circuit technology (digital, analog, opto-elektronic/optisch) and related implementation technologies (CMOS, WSI, M(O)EMS, etc.) for neural hardware
  • Overview of fundamental architectural principles of neurochips, -processors and -computers
  • Assessment criteria and taxonomy for neural HW
  • Presentation and detailed discussion of selected, representative implementations
  • Outlook on new lines in the field, e.g., evolvable hardware, organic computing, and self-monitoring and repairing sensor systems
  • Basic methods of signal analysis and the computation of characteristic and invariant descriptors (features)
  • Processing of signals from single sensors und homogeneous or heterogeneous Sensor-Arrays
  • Dimensionality reduction of high-dimensional sensor data by linear and non-linear methods, e.g. by explicit selection of features
  • Methods der cluster analysis
  • Methods for multi-dimensional sensor data analysis: projection and visualisation, fusion
  • methods for classification of sensor data: statistical pattern recognition, artificial neural networks, Methods of rule-based and fuzzy classification
  • Advanced ptimization methods for parameter- or structure optimization of sensor systems
  • Relations, dependencies, and optimization potential between sensor realization, electronics, and algorithmics.
  • New aspects of reliable sensor systems (self-x properties)
The question of how humans see and represent themselves has been approached via the analogy to artifact and machine, sometimes stressing similarities, sometimes differences. This course discusses pivotal texts from various times in history with the goal to analyze how the

(perceived) relationship between man and machine was, and still is, changing.

Competencies / intended learning achievements

On successfully completing the module students will be able to,
  • prepare and manage a scientific discussion on specific topics related to the module
  • explain fundamental models and methods in human computer interaction (HCI)
  • explain state-of-the-art concepts and methods for designing complex robotic systems
  • characterize the control of biological movement systems and basic methods of soft computation
  • explain methods for controlling complex biologically motivated robots (e.g. humanoid robots)
  • prepare special topics and present them to the audience comprehensibly using electronic media
  • understand the concepts of dedicated neural and bio-inspired hardware and its application potential and limitations
  • understand the design principles of circuits with alternative signal representation and adaptive structures
  • understand the effect of simplified implementations
  • understand the relevant principals and methods from the field of Computational Intelligence/Machine Learning

Requirements for attendance of the module (informal)


Requirements for attendance of the module (formal)


References to Module / Module Number [SO-08-2611-M-6]