188.081 Research seminar for PhD students
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2021W, SE, 2.0h, 3.0EC, to be held in blocked form

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: SE Seminar
  • Format: Distance Learning

Learning outcomes

After successful completion of the course, students are able to

  • find solutions for problems of computer science selected for this particular course
  • subject the developed solutions to a critical analysis

Subject of course

Presentation of challenging topics in the fields of Data Engineering, Information & Knowledge Engineering, Process Engineering, Software Engineering and Web Engineering as well as Information Visualization and Visual Analytics.

More information can be found via http://www.ifs.tuwien.ac.at/ifs

Teaching methods

Didactic approach:
- Individual tasks depending on the task at hand.
- Teamwork is possible after consultation with the supervisor.
- Interactive process in consultation with the supervisor.
- Question hours, discussion rounds as required.

Mode of examination

Immanent

Additional information

 

Preliminary Discussion: 

  • Silvia Miksch (Informationsvisualisierung, Visual Analytics): TBS
  • Stefan Biffl: 04.10.2021 16:30, online via Zoom, Link

ECTS Breakdown       3 ECTS = 75 hours       5   Lectures 25   Literature search and reading 30   Writing a state-of-the art report 5   Paper review 8   Presentation 2   Class discussion ------    ------------------------------------- 75  

 

Please consider the plagiarism guidelines of TU Wien when writing your seminar paper: Directive concerning the handling of plagiarism (PDF)

Lecturers

Institute

Examination modalities

   Evaluation of participation and presentation of discussed problems and solutions

Course registration

Begin End Deregistration end
16.09.2021 00:00

Registration modalities

persönlich bei den jeweiligen BetreuerInnen

Curricula

Literature

Bei der Vorbesprechung

Previous knowledge

  Individual previous knowledge required depending on subject area. - Topics and requirements are published on the respective information page.

Miscellaneous

Language

German