389.186 Signal Processing for Big Data
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020S, SE, 2.0h, 3.0EC

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: SE Seminar

Learning outcomes

After successful completion of the course, students are able to get familiar with a topic in the field "Signal Processing for Big Data" und to give a talk about the mathematical and technical foundations of the topic.

Subject of course

Students will

  • start on a topic with some introductory references
  • find more advanced literature and identify key papers
  • understand principles, methods and potential applications
  • give presentations (25mins + 5mins discussion); talks & slides in English
  • possibly do some Matlab/C-Programming if required for the topic

A list with topics will be posted here on 1 April 2019

https://www.nt.tuwien.ac.at/wp-content/uploads/2019/04/sem.pdf

Short discussion of topics on 4 April 2019, 14:00, in room CG0402.

Deadline: 12 April 2019 (email to norbert.goertz@nt.tuwien.ac.at) for a ranked list of   three  preferred topics.

Dates for seminar presentations: 6 June 2019, from 14:00

(also 14 June, 14:00, if required)

Other presentation dates possible, by mutual agreement (email norbert.goertz@nt.tuwien.ac.at)

Teaching methods

students work independently on a  topic

Mode of examination

Oral

Additional information



Please consider the plagiarism guidelines of TU Wien when writing your seminar paper: http://www.tuwien.ac.at/fileadmin/t/ukanzlei/t-ukanzlei-english/Plagiarism.pdf


Please consider the plagiarism guidelines of TU Wien when writing your seminar paper: https://www.tuwien.ac.at/fileadmin/t/ukanzlei/t-ukanzlei-english/Plagiarism.pdf


Please consider the plagiarism guidelines of TU Wien when writing your seminar paper: https://www.tuwien.at/fileadmin/Assets/dienstleister/Datenschutz_und_Dokumentenmanagement/Plagiarism.pdf

Lecturers

Institute

Examination modalities

presentation and slides

Course registration

Not necessary

Curricula

Literature

No lecture notes are available.

Previous knowledge

Discrete-time signal processing

Language

English