FID3018 Advanced Course in Data Mining and Analytics 7.5 credits
Information for research students about course offerings
The course is offered together with ID2222, period 2.
Content and learning outcomes
Course contents
This course is a graduate reading course that will cover the research works of the last two years in the area of Big Data Mining and Analytics. A particular focus will be given to the algorithms and systems on large scale graph processing, stream processing, social network analytics and decentralized machine learning. Every participant should find their own relevant research literature, read and analyze its contributions, give a presentation on the material and actively contribute to the group discussions, as well as write a short report on the paper.
Intended learning outcomes
After the course the student will be able to discuss, analyze, present, and critically review the very latest research advancements in the areas of Big Data Mining and Analytics and make connections to knowledge in related fields. The student will also be able to assess and evaluate new emerging trends as well as to identify the need for further knowledge in the field.
Literature and preparations
Specific prerequisites
Enrolled as a doctoral student.
Recommended prerequisites
The student should have the general knowledge in Data Mining, corresponding to the master level course ID2222 “Data Mining”.
Equipment
Literature
Latest papers in the area of Data Mining and Analytics from high-quality international venues.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
Examination
- EXA1 - Examination, 7.5 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.
P/F
Other requirements for final grade
The course will be assessed with a Pass/Fail grade, based on a successful presentation, the delivery of a scientifically sound report, and the identification of appropriate papers for the reading list. In addition to this, students must attend at least 75% of all seminars.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
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.