Mining Massive Data Sets
We introduce the participant 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. Participants 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.
Link Analysis -- PageRank
Locality-Sensitive Hashing -- Basics + Applications
Data Stream Mining
Analysis of Large Graphs
More About Link Analysis -- Topic-specific PageRank, Link Spam.
More About Locality-Sensitive Hashing
A course in database systems is recommended, as is a basic course on algorithms and data structures. You should also understand mathematics up to multivariable calculus and linear algebra.
There is a free book "Mining of Massive Datasets, by Leskovec, Rajaraman, and Ullman (who by coincidence are the instructors for this course :-). You can download it at http://www.mmds.org/ Hardcopies can be purchased from Cambridge Univ. Press.
Jure Leskovec, Stanford University
Anand Rajaraman, Stanford University
Jeff Ullman, Stanford University
There will be about 2 hours of video to watch each week, broken into small segments. There will be automated homeworks to do for each week, and a final exam.
Statement of Accomplishment
Participants who successfully complete the class will receive a Statement of Accomplishment signed by the instructors. A level designated "distinction" will also be offered.