autumn 2024
FYS-3012 Pattern recognition - 10 ECTS

Type of course

The course is available as a singular course. The course is also available to exchange students and Fulbright students.

Admission requirements

Admission requirements are a Bachelor's degree in physics or similar education, including specialization in physics worth the equivalent of not less than 80 ECTS credits. Local admission, application code 9371 - singular courses at Master's level.

Course overlap

If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:

FYS-363 Pattern recognision 9 ects
FYS-8012 Pattern recognition 8 ects

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 nonlinear (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 concepts of classification, clustering and dimensionality reduction in data analysis
  • apply advanced methods from pattern recognition to diverse classification, dimensionality reduction and clustering problems
  • compare different algorithms with respect to their strengths and applicability
  • describe important pattern recognition applications in society

Skills - The student can

  • analyse modern pattern recognition methods and apply them in independent practical and theoretical problem solving
  • train and validate a pattern recognition algorithm for a defined task using available data
  • analyse Bayes classifiers in terms of error probabilities
  • design linear classifiers for minimization of squared errors and other criteria
  • design and analyse nonlinear classifiers in the form of neural networks
  • perform feature extraction and data transformation, e.g. using eigenvectors
  • explain different clustering algorithms, and analyse their strengths
  • implement in practice all methods discussed in the course for analysis of data

General competence - The student can

  • report the comparison of several pattern recognition algorithms applied to a practical problem
  • appreciate the importance of pattern recognition in society
  • work with pattern recognition methods for analysis of real data

Language of instruction and examination

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

Information to incoming exchange students

This course is open for inbound exchange student who meets the admission requirements. Please see the Admission requirements’ section. Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty:



Examination: Date: Weighting: Duration: Grade scale:
Off campus exam 14.10.2024 09:00 (Hand out)
28.10.2024 13:00 (Hand in)
5/10 2 Weeks A–E, fail F
School exam 16.12.2024 09:00
5/10 4 Hours A–E, fail F
UiT Exams homepage

Re-sit examination

There is no access to a re-sit examination in this course
  • About the course
  • Campus: Tromsø |
  • ECTS: 10
  • Course code: FYS-3012
  • Earlier years and semesters for this topic