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Content and learning outcomes
Course contents
- Basic definitions of graph theory, strong and weak ties, degree distributions and clustering measures.
- Erdos-Renyi, Wats-Strogatz, configuration model, the small-world effect.
- Random walks on graphs, Page Rank.
- Clustering and community detection.
- Label Propagation, link prediction.
- Distributional semantics, topic modelling, document summarisation.
Intended learning outcomes
After passing the course, the student shall be able to
- explain different fundamental concepts of data mining including information network analysis and mining (e.g., basic concepts of graph theory, network models, algorithms for clustering, community detection, label propagation, link prediction etc.)
- analyse, select, use, and evaluate data mining techniques and algorithms that are based on the above concepts, as well as independently explore existing data mining algorithms and implement them
- communicate findings, results and ideas in a clear, formal way.
Course Disposition
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Literature and preparations
Specific prerequisites
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Recommended prerequisites
Familiarity with the basic probability theory, linear algebra as well as ability to write a non-trivial computer program.
Equipment
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Literature
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Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
A, B, C, D, E, FX, F
Examination
- PRO1 - Project, 3,0 hp, betygsskala: P, F
- TEN1 - Examination, 4,5 hp, betygsskala: A, B, C, D, E, FX, 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.
Opportunity to complete the requirements via supplementary examination
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Opportunity to raise an approved grade via renewed examination
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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 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 ID2211Offered by
Main field of study
Computer Science and Engineering
Education cycle
Second cycle
Add-on studies
No information inserted
Supplementary information
In this course, the EECS code of honor applies, see: http://www.kth.se/en/eecs/utbildning/hederskodex.