Stanford
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Ariel Poler, MBA '94, first heard about something called the World Wide Web in late 1993, about six months before he was due to graduate. He doesn't remember exactly where he had that first encounter with the Internet's graphic interface, but he does remember his reaction. "I was blown away. I said, 'Oh my God, this is it,'" recalls Poler, a native of Venezuela who had come to Stanford hoping to acquire the skills to start his own technology business. He immediately started looking for an opportunity related to the Web. Three months later, he found it.
Working the Web
Over the past two years there has been a close collaboration between the Data Mining Group (MIDAS) and the Digital Libraries Group at Stanford in the area of Web research. It has culminated in the WebBase project whose aims are to maintain a local copy of the World Wide Web (or at least a substantial portion thereof) and to use it as a research tool for information retrieval, data mining, and other applications. This has led to the development of the PageRank algorithm, the Google search engine, the DIPRE algorithm, and a number of other works which represent the cutting edge of research on the Web today (see WebBase Publications). The topics of this class are data mining and information retrieval in the context of the World Wide Web. First, we will cover background material in data mining and information retrieval that is relevant to the class.
CS 349: Data Mining, Search, and the World
Research - Can Polling Location Influence
STANFORD GRADUATE SCHOOL OF BUSINESS—What would you say influenced your voting decisions in the most recent local or national election? Political preferences? A candidate's stance on a particular issue? The repercussions of a proposition on your economic well-being? All these "rational" factors influence voting, and peoples' ability to vote, based on what is best for them, is a hallmark of the democratic process. But Stanford Graduate School of Business researchers, doctoral graduates Jonah Berger and Marc Meredith, and S.
Individually, the code snippets here are in the public domain (unless otherwise noted) — feel free to use them however you please. The aggregate collection and descriptions are © 1997-2005 Sean Eron Anderson. The code and descriptions are distributed in the hope that they will be useful, but WITHOUT ANY WARRANTY and without even the implied warranty of merchantability or fitness for a particular purpose.
Bit Twiddling Hack
www-db.stanford.edu/pub/voy/museum/picture...
The development of the Google algorithms was carried on on a variety of Computers, mainly provided by the NSF-DARPA-NASA-funded Digital Library project at Stanford. Click to see the equipment in its laboratory setting on the basement floor of Gates Information Sciences. Crawling the web to obtain its link structure required an enormous amount of storage in comparison with typical student projects at that time. We show here the original storage assembly, containing 10 4 Gigabyte disk drives, giving 40 Gbytes total.
CS 229: Machine Learning (Course handouts)
Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here . Previous projects: A list of last year's final projects can be found here . Matlab resources: Here are a couple of Matlab tutorials that you might find helpful: http://www.math.ufl.edu/help/matlab-tutorial/ and http://www.math.mtu.edu/~msgocken/intro/node1.html .Research interests: Artificial Intelligence, Machine learning, Unsupervised feature learning and Deep learning, Neuroscience-informed AI, Robotics.
Andrew Ng's Home page
CS345: Data Mining
Readings have been derived from the book Mining of Massive Datasets . Also you will find Chapter 20.2, 22 and 23 of the second edition of Database Systems: The Complete Book (Garcia-Molina, Ullman, Widom) relevant. Slides from the lectures will be made available in PDF format. Students will use the Gradiance automated homework system for which a fee will be charged.
With 15 forewords from John B. Anderson (R,I–IL), Birch Bayh (D–IN), John Buchanan (R–AL), Tom Campbell (R–CA), Greg Aghazarian (R–CA), Saul Anuzis (R–MI), Laura Brod (R–MN), James L. Brulte (R–CA), Tom Golisano (R,I–FL), Joseph Griffo (R–NY), Ray Haynes (R–CA), Bob Holmes (D–GA), Dean Murray (R–NY), Tom Pearce (R–MI), Christopher Pearson (P–VT) “This book describes the ‘Agreement Among the States to Elect the President by National Popular Vote’—an innovative approach that is a politically practical way to achieve the goal of nationwide popular election of the President. It has my enthusiastic support.”
.:: Every Vote Equal ::.
Sergey Brin
A major research interest is data mining and I run a meeting group here at Stanford. For more information take a look at the MIDAS home page or see the datamine maling list achive . Here are some recent publications:Hi there, I am Andreas Weigend. My expertise is in social and mobile technologies and in consumer behavior: I study people and the data they create. In today's increasingly digitized world, consumers are sharing data in unprecedented ways. This Social Data Revolution represents a deep shift in how people make purchasing and lifestyle decisions.