autumn 2020
FYS-8012 Pattern recognition - 10 ECTS

Sist endret: 17.08.2020

Application deadline

Registration deadline for PhD students at UiT - The Arctic University of Norway: September 1st

Application deadline for other applicants: June 1st. Application code 9303 in Søknadsweb.

Type of course

The course is available as a singular course.

Admission requirements

PhD students or holders of a Norwegian master´s degree of five years or 3+ 2 years (or equivalent) may be admitted. PhD students must upload a document from their university stating that there are registered PhD students. This group of applicants does not have to prove English proficiency and are exempt from semester fee.

Holders of a Master´s degree must upload a Master´s Diploma with Diploma Supplement / English translation of the diploma. Applicants from listed countries must document proficiency in English. To find out if this applies to you see the following list:

Proficiency in English must be documented - list of countries


For more information on accepted English proficiency tests and scores, as well as exemptions from the English proficiency tests, please see the following document:

Proficiency in english - PhD level studies

PhD students at UiT The Arctic University of Norway register for the course through StudentWeb .

External applicants apply for admission through SøknadsWeb.

Application code 9303.

All external applicants have to attach a confirmation of their status as a PhD student from their home institution. Students who hold a Master of Science degree, but are not yet enrolled as a PhD-student have to attach a copy of their master's degree diploma. These students are also required to pay the semester fee.

More information regarding PhD courses at the Faculty of Science and Technology is found here.

Course content

The course covers data analysis techniques such as Bayes classifiers, estimation of probability density functions and related non-parametric classification approaches. Further, linear classifiers using least squares are addressed, in addition to simple processing units (neurons) and their extension to artificial neural networks. Linear and non-linear (using kernel functions) support vector machine classifiers are discussed, in addition to feature extraction and data transformation using eigenvector-based methods such as Fisher discriminants. Methods for grouping, or clustering, data are treated in detail, including hierarchical clustering and k-means. Exercises and problem solving, in addition to practical pattern recognition for data analysis using programming, are strongly emphasized. Basic programming skills are required.

Recommended prerequisites

FYS-2006 Signal processing, STA-1001 Probability and statistics

Objectives of the course

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 analysze 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

Language of instruction

The language of instruction is English and all of the syllabus material is in English. Examination questions will be given in English, but may be answered either in English or a Scandinavian language.

Teaching methods

Lectures: 40 hours Exercises: 40 hours


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.


Course overlap

FYS-3012 Pattern recognition 8 stp
  • Om emnet
  • Studiested: Tromsø |
  • Studiepoeng: 10
  • Emnekode: FYS-8012