In the course, the foundations of data mining is studied, with a special focus on information network analysis and mining.
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Content and learning outcomes
- Basic definitions in graph theory, strong and weak bands, grade distribution and clustering measurements.
- Erdos-Renyi, Wats-Strogatz, ccnfiguration models, the effect of a "small world".
- Random walks in graphs, Page Rank.
- Graph clustering, identification of "communities".
- The algorithm "Label Propagation", link prediction.
- Basics of machine learning of graph representations.
Intended learning outcomes
After passing the course, the student shall be able to
- explain different fundamental concepts and algorithms in data mining and basic technologies for analysis and extraction in information networks (for example the fundamental concepts in graph theory, network models, algorithms left graph clustering, identification of "communities", "Label Propagation", link prediction, etc)
- analyse, choose, use, and evaluate technologies for data mining that is based on the above concepts and explore and implement the existing data mining algorithms independently
- communicate findings, results and ideas with clear and formal language.
Literature and preparations
Familiarity with the basic probability theory, linear algebra as well as ability to write a non-trivial computer program.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- PRO1 - Project, 3.0 credits, grading scale: P, F
- TEN1 - Examination, 4.5 credits, grading scale: 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.
The exam is written.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
- 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 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 ID2211
Main field of study
In this course, the EECS code of honor applies, see: http://www.kth.se/en/eecs/utbildning/hederskodex.