Computer Systems Laboratory Colloquium. SCPD - Donald E. Knuth. Jobs. GeorgeKaos review. Data Mining Lecture Note. CS 349: Data Mining, Search, and the World. Tuesdays and Thursdays 4:15 - 5:30 in Bldg 370, Room 370 on the Main Quad Instructors: Sergey Brin and Lawrence Page Tues and Thurs 5:30 - 7:00 or by appointment. sergey@cs.stanford.edu and page@cs.stanford.edu Course Assistant: Diane Tang Gates 416: Mon - Wed 11:15 - 12:15 or by appointment. dtang@cs.stanford.edu Description 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. The topics of this class are data mining and information retrieval in the context of the World Wide Web. Prerequisites A strong knowledge of C.
Very Tentative Syllabus Mailing List. 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. Christian Wheeler, associate professor of marketing, conclude that a much more subtle and arbitrary factor may also play a role—the particular type of polling location in which you happen to vote. It's hard to imagine that something as innocuous as polling location (e.g., school, church, or fire station) might actually influence voting behavior, but the Stanford researchers have discovered just that. Why might something like polling location influence voting behavior?
Stanford Prison Experiment. Bit Twiddling Hack. By Sean Eron Anderson seander@cs.stanford.edu 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.
As of May 5, 2005, all the code has been tested thoroughly. Contents About the operation counting methodology When totaling the number of operations for algorithms here, any C operator is counted as one operation. Compute the sign of an integer The last expression above evaluates to sign = v >> 31 for 32-bit integers. Alternatively, if you prefer the result be either -1 or +1, then use: sign = +1 | (v >> (sizeof(int) * CHAR_BIT - 1)); // if v < 0 then -1, else +1 On the other hand, if you prefer the result be either -1, 0, or +1, then use: Www-db.stanford.edu/pub/voy/museum/picture... CS 229: Machine Learning (Course handouts) Lecture notes 1 (ps) (pdf) Supervised Learning, Discriminative Algorithms Lecture notes 2 (ps) (pdf) Generative Algorithms Lecture notes 3 (ps) (pdf) Support Vector Machines Lecture notes 4 (ps) (pdf) Learning Theory Lecture notes 5 (ps) (pdf) Regularization and Model Selection Lecture notes 6 (ps) (pdf) Online Learning and the Perceptron Algorithm.
(optional reading) Lecture notes 7a (ps) (pdf) Unsupervised Learning, k-means clustering. Lecture notes 7b (ps) (pdf) Mixture of Gaussians Lecture notes 8 (ps) (pdf) The EM Algorithm Lecture notes 9 (ps) (pdf) Factor Analysis Lecture notes 10 (ps) (pdf) Principal Components Analysis Lecture notes 11 (ps) (pdf) Independent Components Analysis Lecture notes 12 (ps) (pdf) Reinforcement Learning and Control Supplemental notes 1 (pdf) Binary classification with +/-1 labels.
Supplemental notes 2 (pdf) Boosting algorithms and weak learning. Andrew Ng's Home page. Andrew Ng is a co-founder of Coursera and the director of the Stanford AI Lab. In 2011 he led the development of Stanford University’s main MOOC (Massive Open Online Courses) platform and also taught an online Machine Learning class that was offered to over 100,000 students, leading to the founding of Coursera. more > Ng’s Stanford research group focuses on deep learning, which builds very large neural networks to learn from labeled and unlabeled data.
Recently, a Stanford team (led by Adam Coates) built the world’s largest deep learning system with over 10 billion learnable parameters trained via back propagation using inexpensive GPU hardware. This work was presented in ICML 2013. more > CS345: Data Mining. Data Mining Winter 2010 Course information: Instructors: Jure LeskovecOffice Hours: Wednesdays 9-10am, Gates 418 Anand RajaramanOffice Hours: Tuesday/Thursday 5:30-6:30pm (after the class in the same room) Room: Tuesday, Thursday 4:15PM - 5:30PM in 200-203 (History Corner). Teaching assistants: Abhishek Gupta (abhig@cs.stanford.edu). Roshan Sumbaly (rsumbaly@cs.stanford.edu). Staff mailing list: You can reach us at cs345a-win0910-staff@lists.stanford.edu Prerequisites: CS145 or equivalent. Materials: Readings have been derived from the book Mining of Massive Datasets. Students will use the Gradiance automated homework system for which a fee will be charged.
You can see earlier versions of the notes and slides covering 2008/09 CS345a Data Mining. Requirements: There will be periodic homeworks (some on-line, using the Gradiance system), a final exam, and a project on web-mining. Projects: Course outline See Handouts for a list of topics and reading materials. Announcements: Important Dates. Stanford Social Innovation Review. .:: Every Vote Equal ::. Sergey Brin. Ph.D. student in Computer Science at Stanford - sergey@cs.stanford.edu Research Currently I am at Google.
In fall '98 I taught CS 349. Data Mining A major research interest is data mining and I run a meeting group here at Stanford. Extracting Patterns and Relations from the World Wide Web by Sergey Brin. World Wide Web Research on the Web seems to be fashionable these days and I guess I'm no exception. GNAT's This project involved indexing multidimensional data for near-neighbor searches.
Near Neighbor Search in Large Metric Spaces by Sergey Brin. I worked on a project with Hector Garcia-Molina involving automated detection of copyright violations. Copy Detection Mechanisms for Digital Documents by Sergey Brin, James Davis, and Hector Garcia-Molina. Miscellaneous Photos My photo collection. HtmlTeX I have found existing tools to convert LaTeX into HTML a little frustrating so I wrote my own simple tool which relies mostly on a LaTeX style file.
Pictures from Art Social Friends and Family End. ☞ Andreas S. WEIGEND, PhD. Groupspace.org.