Course manual 2017/2018
Course content
Linear-non linear parameter models, basic matrix-vector algebra,
maximum likelihood principle, correlated-uncorrelated noise, OLS, GLS,
nuisance parameters, linear (time invariant) filters, t-test, F-test,
confidence intervals, fMRI data model, missing data, MEG/EEG source
localisation. These topics are treated in the form of a series of
lectures alternated with exercises.
Extra topics: L1 en L2 norms.
Objectives
The course treats the theory of parameter estimation problems in
general, but the theory is illustrated extensively by examples from
medical and biological sciences and brain imaging (fMRI and MEG/EEG) in
particular. Linear and non-linear regression analysis is treated, as
well as confidence intervals and significance testing. The goal of the
course is to provide insight into the theory of parameter estimation
and to develop a critical attitude towards its application and
interpretation in order to avoid inconsistent and improper use of the
theory.
Teaching methods
- Lecture
- Computer lab session/practical training
Learning activities
Activity | Hours |
Hoorcollege | 33 |
Practicum | 5.5 |
Tentamen | 5.5 |
Self study | 124 |
Total | 168 | (6 EC x 28 uur) |
Attendance
Requirements concerning attendance (OER-B).
In addition to, or instead of, classes in the form of lectures, the elements of the master’s examination programme often include a practical component as defined in article 1.2 of part A. The course catalogue contains information on the types of classes in each part of the programme. Attendance during practical components is mandatory.
Assessment
Fraud and plagiarism
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
Course structure
| Weeknummer | Onderwerpen | Studiestof |
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Coordinator