Coursera/Stanford “Mining Massive Datasets”, free online course
Top Stanford researchers teach efficient and scalable methods for extracting models and other information from very large amounts of data. Next session of this great course starts Sep 12 on Coursera and is free.
Mining Massive Datasets on Coursera
www.coursera.org/course/mmds
Next session:
Sep 12 - Oct 31, 2015
Instructors
- Jure Leskovec, Stanford University
- Anand Rajaraman, Stanford University
- Jeff Ullman, Stanford University
About the Course
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.
Suggested Readings
There is a free book "Mining of Massive Datasets, by Leskovec, Rajaraman, and Ullman (who are the instructors for this course). You can download it at www.mmds.org/
Hardcopies can be purchased from Cambridge Univ. Press.
Enroll at www.coursera.org/course/mmds
