    Course module   SOWDGCN53  Category   MA (Master)  Language of instruction   Dutch  Offered by   Radboud University; Faculty of Social Sciences; Cognitive Neuroscience;  Lecturer(s)     Academic year   2022   Period   SEM1  (05/09/2022 to 27/01/2023) 
 Starting block   SEM1  
 Course mode   fulltime  
 Remark   Please note: if you do not yet have a master's registration, you are not yet registered for the tests for this course. 
 Remarks     Registration using OSIRIS   Yes  Course open to students from other faculties   No  Preregistration   No  Waiting list   No  Placement procedure    
     
Analysing and modelling neurobiological and behavioural data typically involves mathematical programming, which pertains to mathematical operations that are applied using a digital computing environment (e.g., solving systems of linear equations, eigen decomposition, the Fourier transform). In this course, we will use the Matlab computing environment, which is especially good for analysing and modelling signals (variables that vary as a function of time, space and frequency).


This course has three parts:
 Introduction to Matlab
 Linear algebra (vectors & matrices, transformations, the general linear model, principal component analysis)
 The Fourier transform and convolution
The parts on linear algebra, the Fourier transform, and convolution are tightly linked to the corresponding lectures in Advanced Mathematics (DGCN09).
The teaching is based on tutorial exercises and feedback sessions. The tutorial exercises must be prepared prior to the feedback sessions, such that individual feedback can be provided.

 
No preliminary knowledge is required, but the course will require less investment for students with experience in Matlab and the mathematical operations that are covered in this course. If you have never worked with Matlab, you will learn this in the first part of this course (through selfstudy exercises). The theory behind the mathematical operations is covered in the Advanced Mathematics course.

  


   Recommended materialsArticlesLiterature will be made available via the Brightspace course environment. 


Instructional modesLectureAttendance Mandatory   Yes 

 TestsComputer assignmentsTest weight   1 
Test type   Assignment 
Opportunities   Block SEM1, Block SEM2 
RemarkNOTE: enrollment for a course automatically registers you for its exam. For participating in the resit, register again.


  
 
 