| SEEM5020 Algorithms for Big Data | |||||||||||||||||||||||||||||||||
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TA: Jinchao Huang |
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| Brief Description | |||||||||||||||||||||||||||||||||
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In this course, we will focus on important topics to deal with massive datasets. The topics will cover basic tail bounds frequently used for approximation algorithm design, various sketches for efficient summarization of big data, similarity search techniques like LSH for large-scale data, and topics on dealing with matrix data and graph data. Next, we cover different models like the external memory model (for I/O algorithms), the MPC model (for distributed algorithms), the work-depth model (for shared-memory parallel algorithms), and the corresponding algorithms fitted to such modeling. Finally, some topics on sampling-based large-scale graph algorithm designs will be covered. |
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| Time and Venues | |||||||||||||||||||||||||||||||||
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Class: Friday 1:30pm - 4:15pm, William M W Mong Eng Bldg 703 Office hour: Friday 4:30pm - 5:15pm, William M W Mong Eng Bldg 507 |
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