autumn 2022
FYS-2021 Machine Learning - 10 ECTS

Last changed 30.09.2022

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 + SIVING

Local admission, application code 9391 - enkeltemner i ingeniørfag.


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 supervised and unsupervised learning, both for regression and classification. Supervised methods will include technologies such as decision trees, linear discrimination and neural networks. TUnsupervised methods covered will include machine learning methods based on linear algebra as well as standard clustering methods. 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


Information to incoming exchange students

There are no academic prerequisites to add this module in your Learning Agreement.

Do you have questions about this module?  Please check the following website to contact the course coordinator for exchange students at the faculty:  https://en.uit.no/education/art?p_document_id=510412


Schedule

Examination

Examination systems: Date: Weighting: Duration: Grade scale:
Off campus exam 4/10 A–E, fail F
School exam 6/10 4 Hours A–E, fail F
Coursework requirements – To take an examination, the student must have passed the following coursework requirements:
Assignment 1 Approved/ Not approved
Assignment 2 Approved/ Not approved

More info about the coursework requirements

2 mandatory assignments:

Access to the off campus exam requires passing the first mandatory assignment and access to the final exam requires passing both mandatory assignments.


Re-sit examination

A re-sit examination will not be arranged in this course.
  • About the course
  • Campus: Tromsø |
  • ECTS: 10
  • Course code: FYS-2021
  • Tidligere år og semester for dette emnet