Applied Data Analytics II

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.