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Semester: Sommersemester

5.04.4207 Processing and analysis of biomedical data -  


Veranstaltungstermine

  • Montag: 08:00 - 10:00, wöchentlich (14.10.2019 - 27.01.2020)
  • Donnerstag: 08:00 - 10:00, wöchentlich (17.10.2019 - 30.01.2020)

Beschreibung

This course introduces basic concepts of statistics and signal processing and applies them to real-world examples of bio-medical data. In the second part of the course, recorded datasets are noise-reduced, analyzed, and discussed in views of which statistical tests and analysis methods are appropriate for the underlying data. The course forms a bridge between theory and application and offers the students the means and tools to set up and analyze their future datasets in a meaningful manner.

content:
Normal distributions and significance testing, Monte-Carlo bootstrap techniques, Linear regression, Correlation, Signal-to-noise estimation, Principal component analysis, Confi-dence intervals, Dipole source analysis, Analysis of variance
Each technique is explained, tested and discussed in the exercises.

Lehrende

TutorInnen

Studienbereiche

  • olt707 - Additional module „Specific knowledge”
  • Studium generale / Gasthörstudium

Info-Link
http://www.uni-oldenburg.de/fileadmin/user_upload/physik/PDF/Modulhandbuecher/Modulhandbuch_Fach-Master_Physik_2015_WS.pdf#page=105

Für Gasthörende / Studium generale geöffnet:
Ja

Hinweise zum Inhalt der Veranstaltung für Gasthörende
This course introduces basic concepts of statistics and signal processing and applies them to real-world examples of bio-medical data. In the second part of the course, recorded datasets are noise-reduced, analyzed, and discussed in views of which statistical tests and analysis methods are appropriate for the underlying data. The course forms a bridge between theory and application and offers the students the means and tools to set up and analyze their future datasets in a meaningful manner. content: Normal distributions and significance testing, Monte-Carlo bootstrap techniques, Linear regression, Correlation, Signal-to-noise estimation, Principal component analysis, Confi-dence intervals, Dipole source analysis, Analysis of variance Each technique is explained, tested and discussed in the exercises.

Lehrsprache
englisch

empfohlenes Fachsemester
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Wen3gbmas3zstelkvt5rbaim (inqc5icfoportatcrl-dqnstu+gi0dium@u4h3kools9a.deuxvnx) (Stand: 07.11.2019)