Practical Course Biosignal Processing and Modeling
Lecturer (assistant) |
|
---|---|
Number | 0000002816 |
Type | |
Duration | 4 SWS |
Term | Sommersemester 2018 |
Language of instruction | English |
Position within curricula | See TUMonline |
Dates | See TUMonline |
Dates
- 13.04.2018 11:30-13:00 2001, Bibliothek, Group 1
- 13.04.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
- 20.04.2018 11:30-13:00 2001, Bibliothek, Group 1
- 20.04.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
- 27.04.2018 11:30-13:00 2001, Bibliothek, Group 1
- 27.04.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
- 04.05.2018 11:30-13:00 2001, Bibliothek, Group 1
- 04.05.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
- 11.05.2018 11:30-13:00 2001, Bibliothek, Group 1
- 11.05.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
- 18.05.2018 11:30-13:00 2001, Bibliothek, Group 1
- 18.05.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
- 25.05.2018 11:30-13:00 2001, Bibliothek, Group 1
- 25.05.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
- 01.06.2018 11:30-13:00 2001, Bibliothek, Group 1
- 01.06.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
- 08.06.2018 11:30-13:00 2001, Bibliothek, Group 1
- 08.06.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
- 15.06.2018 11:30-13:00 2001, Bibliothek, Group 1
- 15.06.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
- 22.06.2018 11:30-13:00 2001, Bibliothek, Group 1
- 22.06.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
- 29.06.2018 11:30-13:00 2001, Bibliothek, Group 1
- 29.06.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
- 06.07.2018 11:30-13:00 2001, Bibliothek, Group 1
- 06.07.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
- 13.07.2018 11:30-13:00 2001, Bibliothek, Group 1
- 13.07.2018 13:15-14:45 2001, Bibliothek, MSNE Students only
Admission information
Description
Theoretical lectures and hands-on practical tutorials on state of the art sensor systems, methods in experiment design, offline and online signal processing and pattern recognition/Machine learning
Prerequisites
Programming skills in C/C++ are recommended, background in MATLAB, basic knowledge in statistical signal processing and machine learning
Examination
schriftlicher Bericht und mündlich