Foundations of machine learning, 3 credits

Spring 2024, One-tenth-time, Distance

Semester Spring 2024
Place of study Distance
Pace of study One-tenth-time
Level First cycle
Teaching form Distance
Education Time Mixed-time
Number of Mandatory Sessions 0
Education Language English
Course offering id LIU-1Z029
Period 202403 - 202422
Number of Places 500

Specific requirements

General entry requirements for undergraduate studies
and
English and Mathematics corresponding to the level of Swedish upper secondary education (Engelska 6 and Matematik 3b/3c or Matematik D)
Exemption from Swedish

Selection

Tuition fees

SEK 7100 - NB: Applies only to students from outside the EU, EEA and Switzerland.

If you have questions about the course, contact us

Studievägledning

Kursansvarig ETE370

Course syllabus

Foundations of machine learning, 3 credits

Autumn 2024, One-tenth-time, Distance

Semester Autumn 2024
Place of study Distance
Pace of study One-tenth-time
Level First cycle
Teaching form Distance
Education Time Mixed-time
Number of Mandatory Sessions 0
Education Language English
Course offering id LIU-1Z030
Period 202436 - 202503
Number of Places 500

Specific requirements

General entry requirements for undergraduate studies
and
English and Mathematics corresponding to the level of Swedish upper secondary education (Engelska 6 and Matematik 3b/3c or Matematik D)
Exemption from Swedish

Selection

Tuition fees

SEK 7100 - NB: Applies only to students from outside the EU, EEA and Switzerland.

If you have questions about the course, contact us

Studievägledning

Kursansvarig ETE370

Course syllabus

Foundations of machine learning, 3 credits

Spring 2025, One-tenth-time, Distance

Semester Spring 2025
Place of study Distance
Pace of study One-tenth-time
Level First cycle
Teaching form Distance
Education Time Mixed-time
Number of Mandatory Sessions 0
Education Language English
Course offering id LIU-1Z029
Period 202504 - 202523
Number of Places 500

Specific requirements

General entry requirements for undergraduate studies
and
English and Mathematics corresponding to the level of Swedish upper secondary education (Engelska 6 and Matematik 3b/3c or Matematik D)
Exemption from Swedish

Selection

Tuition fees

SEK 7100 - NB: Applies only to students from outside the EU, EEA and Switzerland.

If you have questions about the course, contact us

Studievägledning

Kursansvarig ETE370

Course syllabus

Course content

The course aims to give an introduction to machine learning, with emphasis on so-called supervised learning. The aim is to understand what machine learning is, what types of machine learning exist, their possibilities and limitations, and to get an overview of common methods and machine learning techniques.

Start the course whenever you want

You can start the course almost anytime you want as the course is an online course with flexible admission. You make the application for the semester you intend to start reading the course. Choose the semester you are interested in above, and you will find the right application opportunity. If you want to start directly, you apply for the current semester. Up to the autumn semester 2021, the course had course code ETE336. When you have received your notification of admission, you find more information on what to do in the next step on the course site: foundations-of-ml.ida.liu.se 

Course outline

The course is distance-based and you can take it in your own pace. It is handled entirely using a web-based course platform (foundations-of-ml.ida.liu.se). The course is based on self-study of the course material and is examined with self-correcting tests and submissions. 

The course is given and examined over the Internet.

Information on general entry requirements 

Please note that you must be able to prove that you fulfill the general entry requirements when applying for the course. If your final school grades are not already on your pages at antagning.se, then you need to upload your upper secondary qualification, or equivalent, at antagning.se in connection with your application.