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Computer vision

Yksittäinen kurssi

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

Vastuuopettaja

Helsingin yliopisto
Laura Ruotsalainen

Hakua koskevat kysymykset

FITech-verkostoyliopisto
Fanny Qvickström, Opintoasioiden suunnittelija
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Kategoria:
ICT-opinnot
Teemat:
5G-teknologia,
Internet-teknologia
Kurssikoodi:
DATA20016
Opintopisteet:
5 ECTS
Hinta:
0 €
Taso:
Opetusaika:
7.9.–21.10.2022
Viimeinen hakupäivä:
30.8.2022
Järjestävä yliopisto:
Helsingin yliopisto
Kohderyhmä:
Aikuisopiskelija,
Tutkinto-opiskelija
Opetustavat:
Lähiopetus
Kieli:
Englanti
Esitietovaatimukset:
Basics of linear algebra, statistics and machine learning, preferably deep learning, required. Capability to use computing platforms.
Kenelle kurssi sopii:
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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