Forschung

Winter Term 2017-18 / Neural Inf Process


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 GUIDELINES  •  MODULE HANDBOOK 2017-18  •  IMPORTANT DATES  •  WEEK PLAN  •  EXAM SCHEDULE

Course title

Mathematical Basics for Computational Neuroscientists

Lecturer
Bethge et al.
Credits
3.0
Course content / topics

Objectives
The main goal of this pre-course is to set the stage for the theory lectures of the Graduate School of Neural Information Processing such as Machine Learning, Neural Coding, and Neural Data Analysis. A strong focus is placed on applied linear algebra and probabilistic modeling. Specific topics include multivariate random variables and matrix factorizations, statistics and optimization, dynamical systems and Fourier transform.

Learning targets
(1) Refresh mathematical background necessary for the lectures of the NIP program
(2) Applied linear algebra, data processing, and probabilistic modeling for computational neuroscience

Prerequisites

Students should have basic knowledge of linear algebra, probability theory, and signal processing. Basic knowledge of programming is required.

Suggested reading
MATLAB demos

Day, time & location

25.+27. Okt, 1.+3. Nov, 8.+10. Nov 2pm