NDAK12003U CMM Projects 1

Volume 2013/2014
MSc Programme in Computer Science
The purpose of the course is to introduce second year MSc students to project oriented work in the context of Computational and Mathematical Modelling, and to prepare the student for the Master Thesis work

The course includes all aspects of project work including from initial brainstorming to finishing report writing.  Subtopics are project management, experimental planning, and scientific writing.
Learning Outcome
After course completion, the student will be able to understand the central aspects of all phases of a scientific project, as well as be able to independently initiate and complete a project.
See Absalon when the course is set up.
We recommend that you have passed several of the following courses: Statistical methods for machine learning, Signal and image processing, Constrained continuous optimization, Computational physics, Advanced topics in data modelling, Dataanalyse, Computergrafik
The course will be a mix of lectures, student presentations, and group work with an outset in the students own projects and reports
  • Category
  • Hours
  • Lectures
  • 21
  • Project work
  • 165
  • Theory exercises
  • 20
  • Total
  • 206
7,5 ECTS
Type of assessment
Practical written examination
Oral defence under invigilation
Group-based projects with individual oral presentation followed by individual examination, graded on Danish ECTS-compliant 7-step scale with internal grading (intern censur). The examination covers the whole scope of the course (see topics and learning objectives), but with special emphasis on the subject of the written report. Submission in Absalon
Exam registration requirements
Active participation in the course
Marking scale
7-point grading scale
Censorship form
No external censorship
Resubmission at next reexamination period. In case of a failed group-project it will be resubmission of a individual report
Criteria for exam assesment

To get top grade the student must:

1. Formulate an operational project plan.
2. Search the relevant literature and write a literature review setting own work in perspective.
3. Be able to formulate what plagiarism is, and demonstrate proper citation and reference style.
4. Solve a selected problem of fair Computational and Mathematical Modelling / eScience content and difficulty.
5. Produce a thorough experiment plan that clearly demonstrates the quality of and highlights boundaries for the solution.
6. Produce a scientific text of fair quality both textually and scientifically.
7. The student must be able to make an oral presentation of own work.