Seminar: Machine Learning in Computer Vision
| Vuosi | Lukukausi | Päivämäärä | Periodi | Kieli | Vastuuhenkilö |
|---|---|---|---|---|---|
| 2015 | syksy | 01.09-08.12. | 1-2 | Englanti | Markus Koskela |
Luennot
| Aika | Huone | Luennoija | Päivämäärä |
|---|---|---|---|
| Ti 14-16 | B119 | Markus Koskela | 01.09.2015-13.10.2015 |
| Ti 14-16 | B119 | Markus Koskela | 27.10.2015-08.12.2015 |
Information for international students
This seminar will be held in English.
Yleistä
Computer vision is a research discipline motivated by the need of building machines that can see. This has turned out to be a hard task: modern computers can be used for a variety of remarkable feats but they are often outperformed in simple vision tasks by a three-year old child.
There has however been considerable recent progress in many subfields of computer vision. For a large part, this is due to advances in machine learning, which is nowadays deployed extensively in all kinds of vision applications.
This seminar can be seen as
- an introduction to modern computer vision and/or
- a course on machine learning with a specific application field
with a uniform probabilistic approach.
Computer vision is a practical discipline in which the results of different algoritms are immediately visible. A lot can be learned by experimenting with real algoritms and real data. Therefore, the following companion project to this seminar is highly recommended: Project in Machine Vision, Spring 2016.
Kurssin suorittaminen
The requirements for passing the seminar include a written report and an oral presentation about a book chapter, commenting on the works of others, and active participation in the discussions.
The seminar will have a fixed weekly meeting time on Tuesdays at 2pm. The first meeting is on September 1st. A detailed schedule will be provided at the first meeting.
Absense from at most two meetings is accepted (and will affect grading).
Kirjallisuus ja materiaali
The seminar will mostly be based on the book:
Prince, S.J.D. Computer Vision: Models, Learning, and Inference. Cambridge University Press, 2012. http://computervisionmodels.com/