Table of contents

The course is connected to the following study programs

Teaching language

English

Recommended previous knowledge

ING100, MAS239 or equivalent.

Learning outcomes

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

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 and learning 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.

Examinations

Project work (experiment) followed by presentation and report (Journal format). Graded assessment.

Student 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.
Last updated from FS (Felles studentsystem) Oct. 15, 2026 8:41:19 AM