Master's Programme in Data Science is responsible for the course.
The course is available to students from other degree programmes but priority will be given to students of the degree programme(s) organising the seminar and to the students who have completed the course Computer Vision DATA20016 .
Prerequisites in terms of knowledge
Basics of computer vision technologies
Prerequisites for students in the Data Science programme, in terms of courses
Orientation to Data Science Studies,
Academic Writing for Students in English-Medium Master's Degree Programmes 1
Prerequisites for other students in terms of courses
Recommended preceding courses
The course Computer Vision, DATA20016, or equivalent.
Specialization studies in Data Science, especially in Machine Learning and Statistical Data Science
The student will learn to find and analyze existing scientific literature around a specific topic, write a scientific report and present the results orally.
The student will also learn to act as a peer for the work of others.
It is recommended that the course DATA20016 has been completed before the seminar or good knowledge on computer vision technologies has been acquired with some other means.
The course will be offered in the spring term 2020.
A seminar course giving a deeper insight into the use of computer vision methods in a certain subject area selected by the student.
A list of possible subject areas and some preselected scientific papers will be given at the course homepage.
Students may select the subject area also based on their interest outside the given topics.
The students will agree upon a specific topic with the instructor, write a scientific report, deliver a scientific oral presentation on the topic and act as a peer.
The students are expected to participate actively in the seminar sessions.
Grading scale 1-5. The grading will combine evaluation of the written report, the oral presentation and other course participation.
Attendance at the seminar sessions is obligatory. Absence from at most two meetings is accepted, but will affect grading.
Written report, oral presentation(s) , acting as a peer for other students, and active attendance at the sessions.
Associate Professor Laura Ruotsalainen