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Computer vision

Individual course

Max amount of FITech students: 10

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

The course consists of 14 lectures, of which the last is given by students based on a group work addressing one of the very recent high quality scienific papers on the field. The course also includes practical exercises.

Exercises (50%) and exam (50%)

The course has to be carried out in the given timetable (exercises and exam). Lectures will be provided only as recordings starting from 2021, but the course will include lessons for questions and discussion. These lessons will also be streamed and recorded.

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
Monica Sandberg, Student services specialist
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ICT Studies
5G technology
Course code:
0 €
Teaching period:
Application deadline:
Application period has ended
Host university:
University of Helsinki
Study is open for:
Adult learner,
Degree student
Teaching methods:
General prerequisites:
Basics of linear algebra, statistics and machine learning, preferably deep learning, required. Capability to use computing platforms.
Study 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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