Computational Problem Solving (5cr)
Code: DT10115-3001
General information
- Enrollment
- 29.04.2025 - 31.10.2025
- Registration for the implementation has begun.
- Timing
- 15.05.2025 - 15.12.2025
- Implementation is running.
- Number of ECTS credits allocated
- 1 - 5
- Local portion
- 0 cr
- Virtual portion
- 5 cr
- Mode of delivery
- Distance learning
- Unit
- Open UAS
- Campus
- Online
- Teaching languages
- English
- Seats
- 5 - 30
- Degree programmes
- Degree Programme in Information and Communication Technology
- Teachers
- Radu Mariescu-Istodor
- Teacher in charge
- Radu Mariescu-Istodor
- Scheduling groups
- Avoimen opiskelijat (Size: 50 . Open UAS : 50.)
- Tutkinto-opiskelijat (myös ristiinopiskelu) (Size: 0 . Open UAS : 0.)
- Groups
-
TOP25_26Other Complimentary Studies Group Semester 2025-2026
-
KAKS25Karelia, Open UAS, All, Fall, 2025
- Small groups
- Open UAS Students
- Degree students
- Course
- DT10115
Evaluation scale
Approved/Rejected
Objective
- Understand and apply mathematical and computational methods for reconstructing movement and drawings from video footage.
- Analyze camera perspectives, distortions, and projections to infer spatial relationships.
- Design, implement, and evaluate simulations and visual explainers.
- Apply optimization techniques such as local search and gradient descent.
- Implement the solution using a programming language (eg. JavaScript or Python).
Content
The course is structured around a single challenge: reconstructing a pen’s path from footage of its movement in front of colored balls. Through this, students will explore:
- Trilateration and geometric localization techniques.
- Perspective analysis and size scaling based on visual input.
- Camera modeling (pinhole camera, lens distortion).
- Map projections (Azimuthal equidistant, Lambert equal-area).
- Simulations and visual debugging (using JavaScript and Three.js).
- Optimization strategies: local search, gradient descent, and genetic algorithms.
- Neural network applications in spatial estimation.
- Signal and image processing techniques for segmentation and motion analysis.
- Problem decomposition and algorithm design for real-world-inspired scenarios.
Location and time
Online
Materials
Original course notes and explainers by the instructor
Szeliski, R. Computer Vision: Algorithms and Applications (optional)
Teaching methods
The course is organized in 2 phases.
Phase 1 - The Competition (16.5.2025 - 15.7.2025) - ENDED
In this phase, students implemented their own solutions and got feedback from the teacher. Students earned badges which translated to credit points.
Competition Link: https://youtu.be/bZ8uSzZv0ew
Discord (#decode-the-drawings channel): https://discord.com/invite/gJFcF5XVn9
Phase 2 - The Course (22.8.2025 - 15.12.2025) - ONGOING
Video Course: https://youtu.be/wH6gZB7m22k
The course asks for a number of 13 homework assignments to be completed. Students can choose which ones they want to solve (max 5, 1 assignment = 1 credit point).
When completed, or if you have any questions, contact your teacher on Discord via a private message or on the #decode-the-drawings public channel: https://discord.com/invite/gJFcF5XVn9
Assessment criteria, approved/failed
The course is passed by earning a single badge during the competition, or doing one single homework assignment during the course.
The number of badges / homework assignments increase the number of credits (max 5 credits).