This class teaches algorithms for extracting models and other information from very large amounts of data. The emphasis is on techniques that are efficient and that scale well.
About the Course We introduce the student to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general. The rest of the course is devoted to algorithms for extracting models and information from large datasets. Students will learn how Google's PageRank algorithm models importance of Web pages and some of the many extensions that have been used for a variety of purposes. We'll cover locality-sensitive hashing, a bit of magic that allows you to find similar items in a set of items so large you cannot possibly compare each pair. When data is stored as a very large, sparse matrix, dimensionality reduction is often a good way to model the data, but standard approaches do not scale well; we'll talk about efficient approaches. Many other large-scale algorithms are covered as well, as outlined in the course syllabus.
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Interested Observer wrote: » Thanks a lot, have signed up for this one.
Rucking_Fetard wrote: » Free programming books
The Duke of Moral Hazard wrote: » Free SAS Tutorials here Free SAS Programming 1 course here
Neon_Lights wrote: » SAS is becoming a lot more visual though and the new VIYA product supports R and Python scripting.
The Duke of Moral Hazard wrote: » Indeed, Viya looks to be a very exciting development. The functionality afforded by being fully extensible with REST APIs should open up new frontiers also.