On successful completion of the course, the student should be able to:
Independently identify real-world application and problems
Define and determine building blocks to accomplish the problem / application
Have hands-on with the different sensors, robots and related equipment to do a specific task experimentally backed by theoretical understanding
Formulate a real-world problem and solve it using the knowledge and the skills developed during the Master’s programme
Have practical application and implications of the knowledge gained in robotics, computer vision, machine learning, control and related domains
Course contents
The course fuses different areas and themes related to robotics, control, computer vision, different sensors and machine learning algorithms for successful implementation of a project work towards solving real world applications. Peer-review process and mini conference where students share their project among peers (and open participants).
Teaching methods
Individual work in groupe project, lectures, exercises.
Examination requirements
Project undertaken must be completed and the outcome described in form of report and a presentation.
Assessment methods and criteria
Project work (experiment) followed by presentation and report (Journal format). Graded assessment.
Evaluation
The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.