FYS-3012 Pattern recognition - 10 stp
The course is available as a singular course. The course is also available to exchange students and Fulbright students.
The course will only be taught if there is a sufficient number of students. Are you interested in following the course, please contact the student advisor as soon as possible.
Knowledge - The student can
- describe the concept of classification of objects in data analysis
- explain clustering of objects in data analysis
- identify and compare different ways to classify and cluster data
- describe important pattern recognition applications in society
Skills - The student can
- analyze Bayes classifiers in terms of error probabilities
- design linear classifiers for minimization of squared errors and other criteria
- design and analyze non-linear classifiers in the form of neural networks
- perform feature extraction and data transformation, e.g. using eigenvectors
- explain different clustering algorithms, and analyze their strengths
- implement in practice all methods discussed in the course for analysis of data
General expertise - The student can
- appreciates the importance of pattern recognition in society
- work with pattern recognition methods for analysis of real data
Portfolio assessment of a take-home examination counting about 25 % and a final oral examination counting about 75 %. All modules in the portfolio are assessed as a whole and one combined grade is given. Assessment scale: Letter grades A-F.
Re-sit examination (section 22): There is no access to a re-sit examination in this course.
Postponed examination (sections 17 and 21): Students with valid grounds for absence will be offered a postponed examination. Both postponed take-home examination and postponed oral examination are arranged during the semester if possible, otherwise early in the following semester.
See indicated sections in Regulations for examinations at the UiT The arctic university of Norway for more information.
Coursework requirements: Access to the final examination requires submission of take-home examination.
Lectures Autumn 2018
f.aman. Anthony Paul Doulgeris
stip. Sigurd Eivindson Løkse