Applied Data Analytics II

Catalog description

The course focuses on data mining and machine learning algorithms for analyzing very large amounts of data. The emphasis will be on Map Reduce as a tool for creating parallel algorithms that can process very large amounts of data. Topics include: Frequent Itemsets and Association Rules; Near Neighbor Search in High Dimensional Data; Locality Sensitive Hashing (LSH); Dimensionality Reduction; Recommendation Systems; Clustering, Link Analysis; Large scale supervised machine learning; Data streams; Mining the Web for Structured Data; and Web Advertising.