Select the semester and course offering above to get information from the correct course syllabus and course offering.
The course studies fundamentals of data mining, data stream processing, and machine learning algorithms for analyzing very large amounts of data. We will use big data processing platforms, such as MapReduce, Spark and Apache Flink, for implementing parallel algorithms, as well as computation systems for data stream processing, such as Storm and InfoSphere.
After this course, students will be able to mine different types of data, e.g., high-dimensional data, graph data, and infinite/never-ending data (data streams); as well as to program and build data-mining applications. They are also expected to know how to solve problems in real-world applications, e.g., recommender systems, association rules, link analysis, and duplicate detection. Moreover, they will master various mathematical techniques, e.g., linear algebra, optimization, and dynamic programming.
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The contents of the course are derived from the following two textbooks:
A. Rajaraman and J. D. Ullman, Mining of massive datasets. Cambridge University Press, 2012 (alternative: J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, 3-rd Ed., Morgan Kaufmann, 2012)
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.
Written examination. Laboratory tasks.
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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 ID2222Computer Science and Engineering
Second cycle
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In this course, the EECS code of honor applies, see: http://www.kth.se/en/eecs/utbildning/hederskodex.