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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.

Choose semester and course offering

Choose semester and course offering to see information from the correct course syllabus and course offering.

Headings with content from the Course syllabus FDD3021 (Spring 2020–) are denoted with an asterisk ( )

Content and learning outcomes

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.

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.

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






Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • 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

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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 web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web FDD3021

Offered by

EECS/Computational Science and Technology

Main field of study

No information inserted

Education cycle

Third cycle

Add-on studies

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Postgraduate course

Postgraduate courses at EECS/Computational Science and Technology