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Generally
FYS-3012 Pattern recognition
Emnebeskrivelse
Work Load
10 credits (ECTS) | Contents
The students are assumed to have fundamental knowledge of probability theory, signal processing and programming (MATLAB or IDL). The subject deals with supervised and unsupervised classification of feature vectors (measured data). Included in the course are Bayes classification (statistical), Linear classifiers (the Perceptron rule, minimization of MSE), nonlinear classifiers (multilayer Perceptron, RBF- network), clustering (sequential and hierarchical methods, K-means) and feature selection/generation. | Form of teaching
Lectures: 45 hr
Exercises: 45 hr
| Compulsory teaching
Exercises | Assessment
1 oral exam (counting75%) plus one 2 weeks home exam counting 20%. | Examination semester
Autumn, Spring | Type of course
Theoretical subject. | Level of course/Year of study
Master level course. | Contact person/lecturer
Arvid Aanstad, tlf: +47 77 64 40 06, e-mail: stud-kons@phys.uit.no | Recommended previous qualifications
FYS-2006 Signal processing, STA-1001 Probability and statistics 1 | Recommended reading/syllabus
Available on request | Language of instruction
Norwegian, or English by request | Course overlap
FYS-363 -1 9 sp
FYS-8012 -1 10 sp | Homepage
http://uit.no/fysikk/fys3012/
| Timetables
http://timeplan.uit.no/emne.php?department=ALLE&module=FYS-3012-1 |
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