Professional studies

Teaching language


Type of course


Cycle/level of course


Recommended year of study


Total number of ECTS

5 cr

Competency aims

The aim of the course is that the student learns
to apply machine learning as a method for
processing data. The course focuses on the use
and treatment of time series data and then
perform regression analysis.

Learning outcomes

After completing the course, the student is
expected to be able to solve specific problems
where supervised learning is used as a training
method to create models that predict future

Course contents

Analytical concepts
Interpretation and processing of input and output
Application of machine learning
Graphical representation of results

Prerequisites and co-requisites

The courses:
Data Processing and Data Science
Descriptive Analytics - Data/Text Mining
Computer vision

Additional information

The student should follow the announced time
for the project, only in exceptional cases can
be changed.

Recommended or required reading

Provided through ItsLearning.

Study activities

  • Lectures - 30 hours
  • Individual- and group instruction - 30 hours
  • Project- and production work/artistic activities - 60 hours
  • Individual studies - 15 hours


  • Total workload of the course: 135 hours
  • Of which autonomous studies: 135 hours
  • Of which scheduled studies: 0 hours

Mode of Delivery

Participation in tuition

Assessment methods

Essays, reports, productions and portfolio

Assessment requirements

To pass the course the student is required to
complete and present a project announced through


  • Westerlund Magnus
  • Scherbakov-Parland Andrej


Westerlund Magnus

Group size

No limit

Assignments valid until

12 months after course has ended

Assessment methods

Date will be announced later - Reports and productions

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