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FYS-8012: Pattern recognition Work Load
10 credits (ECTS) | Type of course
Theoretical subject. | Level of course/Year of study
Master level course. | Language of instruction
Norwegian, or English by request | Examination semester
Autumn, Spring | 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
| Coursework requirements
Exercises
| Exam and assessment
1 oral exam (counting75%) plus one 2 weeks home exam counting 20%. | Contact person/lecturer
Arvid Aanstad, tlf: +47 77 64 40 06, e-mail: stud-kons@phys.uit.no | Recommended reading/syllabus
Available on request | Course overlap
FYS-3012 -1 10 sp |
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