autumn 2018
FYS-2021 Machine Learning - 10 ECTS

  1. Startsida
  2. Emnekatalog
  3. Machine Learning

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.

Admission requirements

Generell studiekompetanse eller realkompetanse + Matematikk R1 eller (S1+S2) og enten Matematikk (R1+R2) eller Fysikk (1+2) eller Kjemi (1+2) eller Biologi (1+2) eller Informasjonsteknologi( 1+2) eller Geologi (1+2) eller Teknologi og forskningslære (1+2).

Local admission, application code 9336 - enkeltemner i realfag.

Course content

The course will introduce the students to the fundamental concepts in machine learning and will study widely used and popular machine learning algorithms for analysing data in the modern society. The course will cover elementary methods for both unsupervised and supervised learning, both for regression and classification. Unsupervised methods covered will include dimensionality reduction based on linear algebra as well as standard clustering methods. Supervised methods will include technologies such as decision trees and linear discrimination. The course will have a significant practical component, in which various applications will be treated in the form of case studies.

Recommended prerequisites

INF-1049 Introduction to computational programming, MAT-1001 Calculus 1, MAT-1002 Calculus 2, MAT-1004 Linear algebra, STA-1001 Probability and statistics

Objectives of the course

Knowledge - The student is able to:

  • Describe fundamental concepts behind machine learning in modern society.
  • Describe a number of machine learning application areas in society.
  • Discuss and select appropriate data sources applicable to a given machine learning approach.
  • Discuss and select appropriate approaches, such as unsupervised versus supervised, when it comes to the choice of machine learning algorithm to use.

Skills - The student is able to:

  • explain the application domains of machine learning methodology and machine learning algorithms for data analysis in society and research.
  • analyse data for knowledge extraction and inference by applying various machine learning methods and algorithms.

General expertise - The student is able to:

  • understand the role of machine learning in modern society in the context of data analysis
  • implement and apply fundamental machine learning methods and algorithms for analysis of data in e.g. Matlab or Python

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: 30 hours

Exercises : 30 hours

Assessment

Portfolio assessment of project assignments counting about 40 % and a final oral examination counting about 60 %. 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 project assignments 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 and approval of project assignments.

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  • About the course
  • Campus: Tromsø |
  • ECTS: 10
  • Course code: FYS-2021
  • Tidligere år og semester for dette emnet