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Høst 2019

FYS-3012 Pattern recognition - 10 stp


The course is administrated by

Institutt for fysikk og teknologi

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.


Course overlap

FYS-8012 Pattern recognition 8 stp

Course contents

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.

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. 

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.

Objective of the course

Knowledge - The student can

Skills - The student can

General expertise - The student can


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.


Date for examination

Take-home examination hand out date 16.10.2019 hand in date 01.11.2019

The date for the exam can be changed. The final date will be announced at your faculty early in May and early in November.