autumn 2017

FYS-3012 Pattern recognition - 10 ECTS

Sist endret: 15.02.2018

The course is provided by

Faculty of Science and Technology

Location

Tromsø |

Application deadline

Applicants from Nordic countries: 1 June for the autumn semester and 1 December for the spring semester. Exchange students and Fulbright students: 1 October for the spring semester and 15 April for the autumn semester. 

Type of course

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.

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 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 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

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: 45 hours Exercises: 45 hours

Assessment

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.

Schedule

Course overlap

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

Recommended reading/syllabus

Information will come