Mining of Massive Datasets

Time 2021-06-02 12:16:31

Web Name: Mining of Massive Datasets

WebSite: http://mmds.org

ID:180367

Keywords:

of,Mining,Datasets,

Description:

The following is the third edition of the book.It contains new material on Spark, Tensorflow, minhashing, community-finding, simrank, graph algorithms, and decision trees.There is a new chapter 13, covering deep learning.We also offer a set of lecture slides that we use for teaching Stanford CS246: MiningMassive Datasets course. Note that the slides do not necessarily cover all the material convered in the corresponding chapters.Download the latest version of the book as a single big PDF file(603 pages, 3.6 MB).The Errata for the third edition of the book: HTML.Download slides (PPT) in French: Chapter 4, Chapter 5, Chapter 8, Chapter 9, Chapter 10. Courtesy of Richard Khoury.Note to the users of provided slides: We would be delighted if you found this our material usefulin giving your own lectures. Feel free to use these slides verbatim, or to modify them to fityour own needs. PowerPoint originals are available. If you make use of a significant portion ofthese slides in your own lecture, please include this message, or a link to our web site:http://www.mmds.org/.Comments and corrections aremost welcome. Please let us know if you are using these materials in your course and we will list and link to your course.Stanford big data coursesCS246CS246: Mining Massive Datasets is graduate level course that discusses data mining and machine learning algorithms for analyzing very large amounts of data. The emphasis is on Map Reduce as a tool for creating parallel algorithms that can process very large amounts of data.CS341CS341 Project in Mining Massive Data Sets is an advanced project based course. Students work on data mining and machine learning algorithms for analyzing very large amounts of data. Both interesting big datasets as well as computational infrastructure (large MapReduce cluster) are provided by course staff.Generally, students first take CS246 followed by CS341.CS341 is generously supported byAmazon by giving us access to theirEC2 platform.CS224WCS224W: Social and Information Networks is graduate level course that covers recent research on the structure and analysis of such large social and information networks and on models and algorithms that abstract their basic properties. Class explores how to practically analyze large scale network data and how to reason about it through models for network structure and evolution.You can take Stanford courses!If you are not a Stanford student, you can still take CS246 as well as CS224W or earn aStanford Mining Massive Datasets graduate certificate by completing a sequence of four Stanford Computer Science courses. A graduate certificate is a great way to keep the skills and knowledge in your field current. More information is available at the Stanford Center for Professional Development (SCPD).Supporting materialsIf you are an instructor interested in using the Gradiance Automated Homework System with this book, start by creating an account for yourself here. Then, email your chosen login and the request to become an instructor for the MMDS book to support@gradiance.com.You will then be able to create a class using these materials. Manuals explaining the use of the system are availablehere.Students who want to use the Gradiance AutomatedHomework System for self-study can registerhere. Then, use the class token 1EDD8A1D tojoin the "omnibus class" for the MMDS book. SeeThe Student Guidefor more information.Previous versions of the bookThe 2nd edition of the book (v2.1)The following is the second edition of the book. There are three new chapters, on mining large graphs, dimensionality reduction, and machine learning. There is also a revised Chapter 2 that treats map-reduce programming in a manner closer to how it is used in practice.Together with each chapter there is aslo a set of lecture slides that we use for teaching Stanford CS246: MiningMassive Datasets course. Note that the slides do not necessarily cover all the material convered in the corresponding chapters.Download the latest version of the book as a single big PDF file (511 pages, 3 MB).Download the full version of the book with a hyper-linked table of contents that make it easy to jump around: PDF file (513 pages, 3.69 MB).The Errata for the second edition of the book: HTML.Download slides (PPT) in French: Chapter 4, Chapter 5, Chapter 8, Chapter 9, Chapter 10. Courtesy of Richard Khoury.Note to the users of provided slides: We would be delighted if you found this our material usefulin giving your own lectures. Feel free to use these slides verbatim, or to modify them to fityour own needs. PowerPoint originals are available. If you make use of a significant portion ofthese slides in your own lecture, please include this message, or a link to our web site:http://www.mmds.org/.Version 1.0The following materials are equivalent to the published book, with errata corrected to July 4, 2012.Download the book as published here (340 pages, 2 MB).

TAGS:of Mining Datasets 

<<< Thank you for your visit >>>

Hot Websites