Judea Pearl Judea Pearl (born 1936) is an Israeli-born American computer scientist and philosopher, best known for championing the probabilistic approach to artificial intelligence and the development of Bayesian networks (see the article on belief propagation). He is also credited for developing a theory of causal and counterfactual inference based on structural models (see article on causality). He is the 2011 winner of the ACM Turing Award, the highest distinction in computer science, "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning".[1][2][3][4] Judea Pearl is the father of journalist Daniel Pearl, who was kidnapped and murdered by militants in Pakistan connected with Al-Qaeda and the International Islamic Front in 2002 for his American and Jewish heritage.[5][6] Biography[edit] Pearl is currently a professor of computer science and statistics and director of the Cognitive Systems Laboratory at UCLA. Books[edit]
hilarymason.com Open Screen Project (Adobe Flash Platform Blog) Today at Mobile World Congress 2010, we made a couple of exciting announcements: advancements to the Adobe Flash Platform including unveiling AIR on mobile devices and Adobe joins LiMo Foundation to bring the Flash Platform to the LiMo Platform. Adobe unveiled AIR running on Android OS. Expected to ship later this year, AIR for Android takes advantage of mobile features from Flash Player 10.1 and is optimized for mobile screens. Developers can use Flash Professional to build apps for the iPhone and deliver those same apps to other platforms on AIR, including Android. As part of the Flash Platform news, we announced the Flash Player 10.1 beta was made available to developers and content providers worldwide, with general availability expected the first half of 2010. Check out one of the content publishers, Sling Media, is using Flash Player and the Flash Platform to pursue their three-screen strategy for laptops, TVs, and mobile devices.
Blurrt – Simply Clever Twitter Analytics | Simply Clever Twitter Analytics Data Mining Research - www.dataminingblog.com Perceptrons in Lisp (A simple machine learning exercise) So having missed Stanford's Machine Learning course (mostly out of laziness - I'm sure it was great) I'm trying to learn this stuff on my own. I'm going through MIT's Machine Learning notes on OpenCourseWare. They're easy [for me] to digest without being insulting, and they help me avoid searching for "The right book" to learn from (a task that would delay my learning anything but make me feel busy). After reading the first two lectures I decided I should stop and practice what I've learned: a simple perceptron learning algorithm. What's a Perceptron anyway? It sounds like a Transformer. We want to choose the variables so that the above term is positive when we'll have a storm, and negative otherwise. More generally, say we have a vector of characteristics . How do we find ? Our learning algorithm will tell us how to choose that . We're going to start with any ol' , say just a vector with 1's in all positions. Let's see it in practice. I [rather foolishly] created my own training data. Edit:
Culture War: Classical Statistics vs. Machine Learning 'Statistical Modeling: The Two Cultures' by L. Breiman (Statistical Science 2001, Vol. 16, No. 3, 199–231) is an interesting paper that is a must read for anyone traditionally trained in statistics, but new to the concept of machine learning. It gives perspective and context to anyone that may attempt to learn to use data mining software such as SAS Enterprise Miner or who may take a course in machine learning (like Dr. From the article, two cultures are defined: "There are two cultures in the use of statistical modeling to reach conclusions from data. Classical Statistics/Stochastc Data Modeling Paradigm: " assumes that the data are generated by a given stochastic data model Algorithmic or Machine Learning Paradigm: "uses algorithmic models and treats the data mechanism as unknown." In a lecture for Eco 5385 Data Mining Techniques for Economists , Professor Tom Fomby of Southern Methodist University distinguishes machine learning from classical statistical techniques: As Breiman states:
define.com: Free Online Dictionary and Thesaurus 7 Steps to Simplify Social Media Strategy for Your Business Social Media Examiner Does your business have a social media strategy? According to research conducted by Constant Contact, over 50% of small businesses need help with social media. While many businesses have a social media presence, many are not engaging on those platforms and thus not meeting their goals. With planning, your small business can use social media effectively. Here are seven steps to a social media strategy for your business. #1: Determine Your Business Objectives for Social Media How do you want to use social media to help your business? Make your goals as concrete, measurable and achievable as possible. Define clear goals for your social media marketing. Here are some objectives commonly identified by small businesses: By setting specific objectives, you establish markers for your business. #2: Know Your Audience Determine the prospects and customers with whom you want to engage on social media. Who is your target audience on social media? #3: Choose Your “Hot Buttons” #6: Plan Your Resource Use