Machine Learning
About the course
The course is available as a singular course. The course is also available to exchange students and Fulbright students.
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.
Admission requirements
Generell studiekompetanse eller realkompetanse + SIVING
Local admission, application code 9391 - enkeltemner i ingeniørfag.
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
Prerequisites
Anbefalte forkunnskaper
INF-0101 Introduction to Programming, INF-0102 Computational Programming, MAT-1010 Calculus, MAT-1020 Linear algebra
Teaching methods
Lectures: 30 hours
Exercises : 30 hours
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.Schedule
The schedules are normally finalized and published well in advance of the start of the semester, often a few weeks beforehand. This gives students the opportunity to organize their studies and prepare for upcoming activities.
It is recommended to check the schedule regularly, as changes may occur.
Information to incoming exchange students
This course is open to incoming exchange students.
Study Level: Bachelor's
Some courses offered to exchange students have specific prerequisites. You can find these listed under the “Admission requirements” section. Please read this information carefully before selecting your courses.
For details on how to apply for exchange, course selection guidelines, or to contact the Incoming Admissions Team, please visit: Admissions for Student Exchange.
Examination
| Oral exam | Duration: 1 Hours Weighting: 5/10 |
Grade: A–E, fail F |
| School exam | Date: 10.12.2026 09:00 Duration: 4 Hours Weighting: 5/10 |
Grade: A–E, fail F |
To take an examination, the student must have passed the following coursework requirements
| Mandatory assignment 1 | Grade: Approved – not approved |
| Mandatory assignment 2 | Grade: Approved – not approved |
Everything you need to know about before, during, and after the exam; registration, absence, appeals, and diplomas: UiT Exams homepage
More info about the coursework requirements
2 mandatory assignments:
Access to the off campus exam requires passing both mandatory assignment.