neu250 - Computational Neuroscience - Statistical Learning

neu250 - Computational Neuroscience - Statistical Learning

Department of Neurosciences 6 KP
Module components Semester courses Sommersemester 2023 Examination
Lecture
  • No access 6.03.250 - Computational Neuroscience - Statistical Learning Show lecturers
    • Prof. Dr. Jochem Rieger
    • Priv.-Doz. Dr. Jörn Anemüller
    • Leo Michalke

    Dates on Tuesday, 30.05.2023 - Friday, 02.06.2023, Monday, 05.06.2023 - Friday, 09.06.2023, Monday, 12.06.2023 - Friday, 16.06.2023, Monday, 19.06.2023 - Wednesday, 21.06.2023 09:00 - 17:00
Exercises
  • No access 6.03.250 - Computational Neuroscience - Statistical Learning Show lecturers
    • Prof. Dr. Jochem Rieger
    • Priv.-Doz. Dr. Jörn Anemüller
    • Leo Michalke

    Dates on Tuesday, 30.05.2023 - Friday, 02.06.2023, Monday, 05.06.2023 - Friday, 09.06.2023, Monday, 12.06.2023 - Friday, 16.06.2023, Monday, 19.06.2023 - Wednesday, 21.06.2023 09:00 - 17:00
Seminar
  • No access 6.03.250 - Computational Neuroscience - Statistical Learning Show lecturers
    • Prof. Dr. Jochem Rieger
    • Priv.-Doz. Dr. Jörn Anemüller
    • Leo Michalke

    Dates on Tuesday, 30.05.2023 - Friday, 02.06.2023, Monday, 05.06.2023 - Friday, 09.06.2023, Monday, 12.06.2023 - Friday, 16.06.2023, Monday, 19.06.2023 - Wednesday, 21.06.2023 09:00 - 17:00
Hinweise zum Modul
Prerequisites
attendance in pre-meeting
Reference text
Course in the first half of the semester Students without Matlab experience should take the optional Matlab course (1. week) of Computational Neuroscience - Introduction
Kapazität/Teilnehmerzahl 18 (
Recommended in combination with neu240 Computational Neuroscience - Introduction
Shared course components with (cannot be credited twice): psy220 Human Computer Interaction
)
Prüfungszeiten
during the course
Module examination
Portfolio, consisting of daily short tests, programming exercises and short reports
Skills to be acquired in this module
Upon successful completion of this course, students
  • have refined their programming skills (in Matlab) in order to efficiently analyze large-scale experimental data
  • are able to implement a processing chain of prefiltering, statistical analysis and results visualization
  • have acquired an understanding of the theoretical underpinnings of the most common statistical analysis methods and basic machine learning principles
  • have practised using existing toolbox functions for complex analysis tasks
  • know how to implement new analysis algorithms in software from a given mathematical formulation
  • can interpret analysis results in a neuroscientific context
  • have applied these techniques to both single channel and multi-channel neurophysiological data

++ Neurosci. knowlg.
+ Scient. literature
+ Social skills
++ Interdiscipl. knowlg.
++ Maths/Stats/Progr.
+ Data present./disc.
+ Scientific English

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