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Introduction to Machine Learning

  • Credits 2.0  credits
  • Education level First cycle
  • Study location Distance with no obligatory meetings
  • Course code DVA133
  • Main area Computer Science

In this course you learn the foundation of Machine Learing.

Today, the explosion of data has created new opportunities to apply machine learning (ML). Handling of the large amounts of data created by the very rapid digitization would not be possible without Machine Learning (ML). The purpose of the course "Introduction to Machine Learning" is to give you the foundation for ML. You will get an introduction to the basic areas of ML: data, statistics and probability for ML.

You will learn to

  • describe fundamentals of machine learning
  • understand the basic of data manipulation and processing
  • perform basic mathematical and statistical operations for machine learning using Python.

Occasions for this course

  • Spring semester 2026

    Scope

    2.0 credits

    Time

    2026-01-19 - 2026-06-07 (part time 10%)

    Education level

    First cycle

    Course type

    Freestanding course

    Application code

    MDU-14531

    Language

    English

    Study location

    Independent of location

    Teaching form

    Distance learning
    Number of mandatory occasions including examination: 0
    Number of other physical occasions: 0

    Course syllabus & literature

    See course syllabus and literature list (DVA133)

    Specific requirements

    Basic eligibility and Mathematics 3b or 3c or Mathematics C

    Selection

    Upper secondary (high school) grades, Swedish Scholastic Aptitude Test, University credits

Spring semester 2027

  • Spring semester 2027

    Scope

    2.0 credits

    Time

    2027-01-18 - 2027-06-06 (part time 10%)

    Education level

    First cycle

    Course type

    Freestanding course

    Application code

    MDU-20021

    Language

    English

    Study location

    Independent of location

    Teaching form

    Distance learning
    Number of mandatory occasions including examination: 0
    Number of other physical occasions: 0

    Course syllabus & literature

    See course syllabus and literature list (DVA133)

    Requirements

    Basic eligibility and Mathematics 3b or 3c or Mathematics C Or: Mathematics – Further level 1b or Mathematics – Further level 1c

    Selection

    Upper secondary (high school) grades, Swedish Scholastic Aptitude Test, University credits

Questions about the course?

If you have any questions about the course, please contact the Course Coordinator.

Professor

Shahina Begum

+4621107370

shahina.begum@mdu.se