课程名称: Mining Massive Datasets（海量数据挖掘）/斯坦福大学

课程主页: 官网已下架

所在平台: Coursera

课程类别: 数据分析Data Analysis

大学或机构: 斯坦福大学

讲师： Jeff Ullman,Anand Rajaraman,Jure Leskovec

授课语言： 英语

提供字幕： 英文

课程文件大小： 1.79GB

课程介绍： 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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