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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

Termin

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

Examiner
No information inserted
Course coordinator
No information inserted
Teachers
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–)
Headings with content from the Course syllabus FDD3021 (Spring 2020–) are denoted with an asterisk ( )

Content and learning outcomes

Course disposition

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.

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)

Postgraduate course

Postgraduate courses at EECS/Computational Science and Technology