neu250 - Computational Neuroscience - Statistical Learning (Veranstaltungsübersicht)

attendance in pre-meeting

Course in the first half of the semester

Students without Matlab experience should take a Matlab course (e.g. neu710) first

Recommended in combination with neu240 Computational Neuroscience - Introduction Shared course components with (cannot be credited twice): psy220 Human Computer Interaction

during the course

Portfolio, consisting of daily short tests, programming exercises and short reports

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