Stanford AI classes
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I enjoyed how the 3.16 section of the Stanford Artificial Intelligence class presented the Bayes theorem. Instead of giving a formula and expecting the alumni to apply it, they gave us a problem that the Bayes theorem would solve and expected, I believe, that we figured it out ourselves. Being as I am counting-challenged, it took me a while to figure out a way of solving it that was simple enough that I could be reasonably sure of my results. It turned out to be a very interesting detour.
What’s on this page? I’m interested in producing complexity out of simple parts. This page contains bookmarks that I collected while working on games; I did not write most of the content linked from here. As a result the set of links here reflects the types of things I needed to know: only a few specific topics (not everything related to game programming), general ideas instead of platform-specific information (graphics, sound, compilers), and ideas and designs instead of source code (I find it easier to go from an idea to code than from code to an idea). Other sites, like Gamedev Tuts+ , Gamedev , and Gamasutra , cover lots more topics than mine does.
Recently Stanford has started a new initiative to bring free classes to the public. From what I’ve seen from statistics, this venture has been extraordinarily successful with over 100,000 sign ups. Most likely only a fraction went through with the class, but that’s still a lot of people, especially for the first time. There has been quite a lot of press about these classes, but none seem to take into account the effects it has on the students that attend Stanford.
My flight to Australia will be tomorrow, so this post is the one ahead of schedule. mlpy - Machine Learning Python : mlpy is a free Python module for Machine Learning. It facilitates classification, regression, clustering and feature selection in Python.
Web Update MITx is beginning of sweeping new online push Editor’s Note: The Tech recognizes that the announcement of MITx constitutes a development that will affect students and faculty in important ways.
At the end of July 2011, Stanford University announced that three introductory one-term undergraduate courses would be available free as online distance learning courses during the October to December 2011 term. Each course is taught be people who are leading figures in their fields, and in some case more-or-less the leading figures. Here are links to the descriptions of each of the courses: Machine Learning , taught by Professor Andrew Ng, Director of the Stanford Artificial Intelligence Lab, which is the main AI research organisation at Stanford University; Database Design , taught by Jennifer Widom, Professor and Chair of the Computer Science Department at Stanford University; Artificial Intelligence (AI) , taught by Sebastian Thrun, Research Professor of Computer Science at Stanford University, and Peter Norvig, Director of Research at Google (who was a keynote speaker at the 2007 ALT Conference ).
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December 16, 2011 at 6:58 am 160,000 Enroll Stanford’s Online AI Course—Is the University Obsolete? @aiclass: “Amazing we can probably offer a Master’s degree of Stanford quality for FREE. HOW COOL IS THAT?”—September 23, 2011 Mark blogged about Stanford’s online Artificial Intelligence course in August .
Game theory is the scientific study of strategically interdependent decision making. While logically demanding, this website makes learning the field easy. Based off of the best-selling Game Theory 101 textbook , my video lectures go at your pace, carefully explaining all the important points you need to know to understand this new language. If you are new, click here for your first lesson , use the bar at the top to navigate through all my lectures and more, or select one from the introductory course below: The Basics @import url( http://www.google.com/cse/api/branding.css ); <p style="text-align:right;color:#A8A8A8"></p>
In the August 15, 2008 Freakonomics Blog entry , guest author Alon Nir poses the question of determining the best strategy for the game Beauty and the Geek , as seen on TV. Roughly, the rules of Nir's version of the game is as follows: There are 7 teams playing. It is common knowledge that 3 teams have strength 4, one team has strength 2, and three teams have strength 1, in the sense that the ratio of two teams strength is equal to the ratio of the probabilities that each will prevail in a contest between them. So a team with strength 4 wins 4/5 of the time against a team with strength 1. In each round of the game, first there is a set of two contests, for each one a winner is chosen according to the strength odds. The same team may win both contests.
Apples, screwdrivers and desks: a comparative review of three Stanford free online computer science coursesGuest Contribution by Gundega Dekena [Update posted by Seb Schmoller on 12 July 2012. Note that Gundega now works for Udacity, the company that developed from the AI course. Read how she became part of the Udacity team on the Udacity blog .] Gundega Dekena is a self taught Linux administrator and web programmer, based in Riga, Latvia. She has been studying all three of the October to December Stanford online computer science courses in parallel - Introduction to Artificial Intelligence (AI), Introduction to Machine Learning (ML), and Introduction to Databases (DB) - putting her in a good position to compare and contrast them.
http://graphics.cs.cmu.edu/projects/crossDomainMatching/ Presented at SIGGRAPH Asia, 2011 People A data-driven technique to find visual similarity which does not depend on any particular image domain or feature representation. Visit the webpage to see some cool results and applications.
I wrote this applet to explore how particle filters work -- partially in an effort to improve my FastSLAM implementation and partially for another project I am working on. Wikipedia has a nice article on particle filters if you would like more background. In FastSLAM, the particle filter is used to represent the robot localization.
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.