Mining of Massive Datasets

Posted by markhkim on Tuesday November 15, 2016 @07:00AM
from the mmds.org dept.

http://www.mmds.org/

( 7 comments | 109 points | on HN )

Re:Mining of Massive Datasets by joaorico on Wednesday November 16, 2016 @04:09AM (#12965496)

This coursera course was taken down, but it is now up and running at lagunita.stanford.edu [0], which uses edx's open source platform [1]. The same happened to other stanford courses previously on coursera, you can find them here [2], including Compilers, Automata Theory, and Convex Optimization.

[0] https://lagunita.stanford.edu/courses/course-v1:ComputerScie...

[1] https://open.edx.org/ https://github.com/edx/edx-platform

[2] https://lagunita.stanford.edu/courses

[ Parent | on HN ]

Re:Mining of Massive Datasets by PaulHoule on Tuesday November 15, 2016 @06:45PM (#12963374)

What amazed me is how much of this is 1990s stuff.

[ Parent | on HN ]

Re:Mining of Massive Datasets by cantagi on Wednesday November 16, 2016 @04:17AM (#12965521)

How do you mean? Do you know of any up-to-date books on large scale data mining?

[ Parent | on HN ]

Re:Mining of Massive Datasets by mrcactu5 on Wednesday November 16, 2016 @12:01AM (#12964641)

massive is an understatement. I have only dealt with puny GB sized data sets. They deal with vectors which cannot fit into main memory.

[ Parent | on HN ]

Re:Mining of Massive Datasets by infinitone on Friday November 18, 2016 @01:41PM (#12989036)

Yes, in general what they refer to are things like the IRS Tax records (250 TB), Yahoo Ad data (900 TB). You just can't use a single machine to work with such data.

[ Parent | on HN ]

Re:Mining of Massive Datasets by leksak on Tuesday November 15, 2016 @03:08PM (#12961697)

Deadlines are at the 29th of November. Best of luck

[ Parent | on HN ]

Re:Mining of Massive Datasets by nthcolumn on Wednesday November 16, 2016 @11:20AM (#12968421)

tldr; parallel map reduce. ;)

[ Parent | on HN ]