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Computer vision

Individual course

Max amount of FITech students: 10

Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course.

Mastering the prerequisite skills is very important in order to complete this course.

The course teaches the basics of computer vision, most important traditional methods as well as the most recent state-of-the-art methods based on deep learning.

Learning outcomes

The student will learn the important terms and concepts related to computer vision, retrieve their mathematical backgrounds for computer vision, process images for good quality, know and use the theoretical basis and most important algorithms for computer vision, know the state-of-the-art methods and applications using the algorithms.

Course material

The teacher provides recordings and slides from lectures, an open-source book is used (freely available for everyone). It is recommended to use also another book available at the university’s electronic library.

Matlab is used in the exercises. It can be replaced with any other tool, but this could cause a bit extra work.

Completion methods

  • Lectures Wed 12-14, Fri 12-14 (Material/Recordings will be available after the lectures)
  • Exercises Wed 16-18

More information of the University of Helsinki course page.

You can get a digital badge after completing this course.

Image processing Object detection SLAM Linear algebra Probability statistics Deep Learning

Responsible teacher

University of Helsinki
Laura Ruotsalainen

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
Application period has ended
Application period has ended
ICT Studies
5G technology,
Internet technology
Course code:
Study credits:
0 €
Course level:
Teaching period:
Application deadline:
Application period has ended
Host university:
University of Helsinki
Who can apply:
Adult learner,
Degree student
Teaching method:
Teaching language:
General prerequisites:
Basics of linear algebra, statistics and machine learning, preferably deep learning, required. Capability to use computing platforms.
Course suitable for:
Advanced Data Science and Computer Science master's students (2 year) or doctoral students, others with enough knowledge in mathematics and machine learning
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