Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Autumn 2021
Content and learning outcomes
The course will start with a part, where we study how a scientific article is constructed. The students will then, in groups of 2-5, choose articles in their sub-track (machine learning, natural language processing or bioinformatics), implement the method in the article and recreate the experiment. The type of project therefore will vary depending on sub-track, but the intended learning outcomes are the same for all three sub-tracks. The aim of the course is to bridge the gap between the courses in each sub-track and the degree project.
Intended learning outcomes
After passing the course, the students should be able to:
- read scientific articles critically
- reproduce methods in articles
- plan and carry out work in a group.
Preparations before course start
The student should have completed most of the courses in one of the subtracks of the track Data Science in the Computer Science masters programme.
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Support for students with disabilities
Students at KTH with a permanent disability can get support during studies from Funka:
Examination and completion
- PRO1 - Project report, 3.5 credits, Grading scale: P, F
- PRO2 - Oral evaluation, 4.0 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.
- 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.
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28 Aug 2023
- DSPHT23 Autumn 2023-50418
Language Of Instruction