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Topic | Student Name | Date | Presentation | |
|---|---|---|---|---|
| 1 | Distributed file systems and map-reduce as a tool for creating parallel algorithms that succeed on very large amounts of data | Yiting Xia | 2/6/2015 | |
| 2 | Similarity search, including the key techniques of min-hashing and locality sensitive hashing | 2/13/2015 | ||
| 3 | Data-stream processing and specialized algorithms for dealing with data that arrives so fast it must be processed immediately or lost | 2/20/2015 | ||
| 4 | The technology of search engines, including Google’s Page Rank, link-spam detection, and the hubs-and-authorities approach | 2/27/2015 | ||
| 5 | Frequent-itemset mining, including association rules, market-baskets, the A-Priori Algorithm and its improvements | 2/6/2015 | ||
| 6 | Algorithms for clustering very large, high-dimensional datasets | 3/13/2015 | ||
| 7 | Two key problems for Web applications: managing advertising and recommendation systems | 3/20/2015 | ||
| 8 | Algorithms for analyzing and mining the structure of very large graphs, especially social-network graphs | 3/27/2015 | ||
| 9 | Techniques for obtaining the important properties of a large dataset by dimensionality reduction, including singular-value decomposition and latent semantic indexing | 4/3/2015 | ||
| 10 | Machine-learning algorithms that can be applied to very large data, such as perceptrons, support-vector machines, and gradient descent | 4/10/2015 |
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