Kaisa_2012_3_photo by Veikko Somerpuro

1.5.2020 at 09:00 - 31.5.2020 at 23:59



Master's Programme in Data Science is responsible for the course.

The course belongs to Specialization Studies of Data Science.

The course is available to students from other degree programmes also, but priority will be given to students of the degree programme(s) organising the course.

Prerequisites in terms of knowledge

Programming skills are required.

Prerequisites for students in the Data Science programme, in terms of courses

DATA11002 Introduction to Machine Learning

Prerequisites for other students in terms of courses

DATA11002 Introduction to Machine Learning

Recommended preceding courses

DATA12001 Advanced Course in Machine Learning or DATA20001 Deep Learning

At the first period of the second year of Master's studies. Please see the courses required as prerequisites.

Course will be offered yearly at the first period.

The student will learn the basics for retrieving knowledge from images using computer vision including

camera models, image processing, feature detection and matching . The course will also teach basics of two

important applications of computer vision; Simultaneous Localization and Mapping (SLAM) and object detection.

Due to current COVID-19 situation general examinations in lecture halls are cancelled. You can check the completion method from the course page or contact the teacher to ask about alternative completion methods. General exams last 3 hours and 30 minutes. Renewal exam (marked with "(U)") is the first general exam after the course and also a renewal exam of course exam(s). In a renewal exam the points student has earned during the course are taken into account. Exams marked with "(HT)" are allowed only to students who have completed the obligatory projects or other exercises included in those courses. Exams marked with "(HT/U)" are renewals to students who have completed the obligatory projects during the course. General exams might cover different area than the lectured course. Check the course web page and contact the responsible teacher if in doubt.

The course will include lectures, 7 weeks, 2*2*45 minutes / week and exercises 2*45 minutes /week.

Active completion of the exercises is required (75 % of given exercises). The course includes an exam.

Associate Professor Laura Ruotsalainen