|
| Udgave: |
Efterår 2012 NAT |
| Point: |
7,5 |
| Blokstruktur: |
1. blok |
| Skemagruppe: |
B |
| Fagområde: |
dat |
| Varighed: |
Full quarter |
| Institutter: |
Datalogisk Institut |
| Studieordning: |
Datalogi Kandidat |
| Uddannelsesdel: |
Kandidat niveau |
| Kontaktpersoner: |
Sune Darkner, darkner@diku.dk |
| Andre undervisere: |
Francois Lauze, |
| Skema- oplysninger: |
 Vis skema for kurset
Samlet oversigt over tid og sted for alle kurser inden for Lektionsplan for Det Naturvidenskabelige Fakultet Efterår 2012 NAT |
| Undervisnings- form: |
The course will be a mix of lectures, student presentations, and group work with an outset in the students own projects and reports |
| Formål: |
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 |
| Indhold: |
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.
|
| Kompetence- beskrivelse: |
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.
|
| Målbeskrivelse: |
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.
|
| Lærebøger: |
Notes |
| Tilmelding: |
Via KUnet from May 15th to June 1st. |
| Faglige forudsætninger: |
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. |
| Formelle krav: |
None |
| Eksamensform: |
Group-based project 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.
Reexamination: resubmission at next reexamination period.
|
| Eksamen: |
Opgave afleveres den 2. november og mundtlig prøve den 9. november 2012.
Reeksamen: Genaflevering af opgave den 1. februar 2013.
|
| Kursus hjemmeside: |
 |
| Bemærkninger: |
Each week will consist of 90 min. lecture including presentation or group work, and 45 min. supervision. Preparation time for the student is expected to be about 17 hours per week. |
| Undervisnings- sprog: |
Kun engelsk
|
| Sidst redigeret: |
24/8-2012 |