The students meet at regular seminar sessions in small groups with a supervisor (3-7 participants). On each occasion, EVERY participant (student and supervisor) presents a recent article within the specialized topics of the focus group. The presentation should include a critical analysis of the work, followed by a group discussion.
FDD3021 Survey group on select topics in computer science 6.0 credits
This course provides a forum for students to digest trending and impactful scientific publications covering a select range of topics related to their research. Meeting regularly in small groups, each student presents a paper and participates in the following discussion.
Information per course offering
Information for Spring 2024 Start 18 Mar 2024 programme students
- Course location
KTH Campus
- Duration
- 18 Mar 2024 - 3 Jun 2024
- Periods
- P4 (6.0 hp)
- Pace of study
33%
- Application code
61128
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
- No information inserted
- Planned modular schedule
- [object Object]
- Schedule
- Schedule is not published
- Part of programme
- No information inserted
Contact
Kevin Smith (ksmith@kth.se)
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus FDD3021 (Spring 2020–)Information for research students about course offerings
All year
Headings with content from the Course syllabus FDD3021 (Spring 2020–) are denoted with an asterisk ( )
Content and learning outcomes
Course disposition
Course contents
Specialized subjects related to data science and machine learning
Intended learning outcomes
On successful completion of the course, the student should be able to:
- Critically read research articles that treat topics within their specialization and explain their essence to other students,
- Select relevant and high quality articles from the scientific literature for presentation
- Discuss articles with respect to the impact, approach, evaluation methodology, and conclusions.
Literature and preparations
Specific prerequisites
The student should carry out research on PhD level within computer vision / machine learning or a related field.
Recommended prerequisites
None
Equipment
None
Literature
None
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
P, F
Examination
- EXA1 - Report writing, 6.0 credits, grading scale: P, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
EXA1 - Examination, 6.0 credits, Grading Scale P,F
Other requirements for final grade
Active participation in at least 18 sessions including presentation at all sessions. A brief 1 paragraph written summary of each paper should be submitted to the supervisor and recorded.
Opportunity to complete the requirements via supplementary examination
No information inserted
Opportunity to raise an approved grade via renewed examination
No information inserted
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Main field of study
This course does not belong to any Main field of study.
Education cycle
Third cycle
Add-on studies
No information inserted
Contact
Kevin Smith (ksmith@kth.se)