Prerequisites: Basic courses on design and analysis of algorithms and data structures. Mathematical and programming maturity.
Fundamentals of data analysis.
Objectives: Upon completion of this course the student will have reliably demonstrated the ability to design, analyze and implement algorithms for massive data sets using
state-of-the-art algorithmic techniques in the area. Furthermore, the student will be able to understand: i) storage strategies that are suited for large-scale datasets (e.g. distributed, unstructured); ii)
alternative processing models that are relevant to big data; iii)
fundamentals of large-scale data mining.
Finally, the student will acquire basic knowledge of experimental algorithmic techniques and data analysis.
Topics: Large-Scale Data Mining
Models, Algorithms, Storage Techniques for Massive Datasets
Books: J. Leskovec, A. Rajaraman, J. D. Ullman. Mining of Massive Datasets. 2nd Edition.
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Last modified on Tuesday, 19 May 2020 13:30