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. more >
Engineering Everywhere | CS229 - Machine Learning
Ng's research is in the areas of machine learning and artificial intelligence. He leads the STAIR (STanford Artificial Intelligence Robot) project, whose goal is to develop a home assistant robot that can perform tasks such as tidy up a room, load/unload a dishwasher, fetch and deliver items, and prepare meals using a kitchen. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. This is in distinct contrast to the 30-year-old trend of working on fragmented AI sub-fields, so that STAIR is also a unique vehicle for driving forward research towards true, integrated AI.
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. 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: sign = (v ! Detect if two integers have opposite signs f = v && !
An AI That Can Mimic Any Artist · cat /var/log/life
19 Dec 2015 Take a look at the following two pictures. One was painted by contemporary artist Leonid Afremov, and the other was painted by an algorithm mimicking his style. The first image is the Afremov’s Rain Princess, and the second image is an imitation. What’s fascinating is that a computer algorithm automatically “painted” the imitation, given only a photograph of the dome and the image of Rain Princess as input. The algorithm used to produce the above image is described in full in a paper titled A Neural Algorithm of Artistic Style. In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. The algorithm is based on one key insight — neural networks trained to perform object detection end up learning to separate content from style. At a high level, the general technique is pretty straightforward. The implementation works pretty well on still images.
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
Artificial Intelligence
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? But Stanford Graduate School of Business researchers, doctoral graduates Jonah Berger and Marc Meredith, and S. 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? Using data from Arizona's 2000 general election, Berger, Meredith, a visiting lecturer at MIT, and Wheeler discovered that people who voted in schools were more likely to support raising the state sales tax to fund education. This effect persisted even when the researchers controlled for—or removed the possibility of—other factors such as: Where voters lived.
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. 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 Introduction: 1 Data Mining: 5 Publications of IBM's QUEST project 10/1 Market Basket (slides) R. Mailing List
