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  • Blog Post: Machine Learning Trends from NIPS 2014

    This blog post is authored by John Platt , Deputy Managing Director and Distinguished Scientist at Microsoft Research. I just returned from the Neural Information Processing Systems (NIPS) 2014 conference , which was held this year in Montreal, Canada. NIPS is one of the two main machine learning...
  • Blog Post: Bing brings the world’s knowledge to your Office documents

    Imagine your child is writing a report about Abraham Lincoln, they just started and so far they’ve typed: “Lincoln was the 16th president of United States. He was born in…” but then realize they’ve forgotten when Honest Abe was born. Ordinarily, they would have to leave...
  • Blog Post: Advancing Research in Sign Language Recognition

    Re-post of a recent article that ran on the An estimated 360 million people worldwide suffer from hearing loss. But a majority of hearing individuals do not understand sign language. So communication between the hearing and the deaf can be challenging. Now researchers are poised to make such interactions...
  • Blog Post: Machine Learning – Hype or Reality? Microsoft ML Experts Weigh In

    The recent Practice of Machine Learning Conference at Microsoft concluded with a lively panel discussion moderated by principal researcher Misha Bilenko on the topic of: "Are We at Peak ML, or at the Start of AI Takeover? Hype vs. Reality of Machine Learning.” Our panelists were: ...
  • Blog Post: AzureML Web Service Parameters

    Overview AzureML Web Service APIs are published from Experiments that are built using modules with configurable parameters. There is often a need to change the module behavior during Web Service execution. The Web Service Parameters feature enables this functionality. A common example is setting...
  • Blog Post: Rapid Progress in Automatic Image Captioning

    This blog post is authored by John Platt , Deputy Managing Director and Distinguished Scientist at Microsoft Research. I have been excited for many years now in the grand challenge of image understanding. There are as many definitions of image understanding as there are computer vision researchers...
  • Blog Post: From Data to Operationalized ML in 60 Minutes!

    This blog post was co-authored by Debi Mishra , Jacob Spoelstra and Dmitry Pechyony of the Information Management & Machine Learning team at Microsoft. Microsoft has a strong track record for crafting tools such as our Office apps or Visual Studio which millions of users find relatively easy to...
  • Blog Post: Free webinar: Operationalizing R as a Web Service

    R is the most widely used language today for machine learning, but its power is sometimes limited by gaps in the technology meant to bring it to life. In this webinar, learn how you can use your existing skills in R in new ways, including deploying models as web services with a few clicks. The first...
  • Blog Post: Forget the pollsters: Microsoft's Bing predicted midterm election with 95% accuracy

    This is a re-post of an article from NetworkWorld. The search engine continues its track record of astonishingly accurate predictions. "Now that the dust has settled from the elections, Bing Predict has won out again with a 95% accuracy rate in calling the House, Senate, and Governor's...
  • Blog Post: How We Share Machine Learning Knowledge at Microsoft

    We recently concluded the Fall 2014 edition of our Practice of Machine Learning Conference (PMLC). Over 1,700 Microsoft employees attended the two day event, which featured 60 talks on a broad spectrum of areas ranging from new algorithms to ML applications such as anomaly detection and fraud. Tutorials...
  • Blog Post: Microsoft adds free tier to Azure Machine Learning

    Starting today, we made it easier than ever for anyone to try Azure Machine Learning. Our service is now available to test free of charge without a subscription or credit card – all you need to get going is a Microsoft account! You can read more about this announcement, made at the PASS Summit...
  • Blog Post: Anomaly Detection – Using Machine Learning to Detect Abnormalities in Time Series Data

    This post was co-authored by Vijay K Narayanan , Partner Director of Software Engineering at the Azure Machine Learning team at Microsoft. Introduction Anomaly Detection is the problem of finding patterns in data that do not conform to a model of “normal” behavior. Detecting such deviations...
  • Blog Post: The Ins and Outs of Azure Stream Analytics – Real-Time Event Processing

    Earlier this week, at TechEd Europe 2014 in Barcelona, we announced the preview of Azure Stream Analytics . Azure Stream Analytics is a cost-effective event processing engine that helps uncover real-time insights from devices, sensors, infrastructure, applications and data quickly and easily. Learn...
  • Blog Post: The Ins and Outs of Azure Data Factory – Orchestration and Management of Diverse Data

    Earlier this week, at TechEd Europe 2014 in Barcelona, we announced the preview of Azure Data Factory . Azure Data Factory enables information production by orchestrating and managing diverse data. Learn about the ins and outs of this new service here . ML Blog Team
  • Blog Post: Embracing Uncertainty – Probabilistic Inference

    This is the second of a 2-part blog post by Chris Bishop , Distinguished Scientist at Microsoft Research. The first part is available here . Last week we explored the key role played by probabilities in machine learning, and we saw some of the advantages of arranging for the outputs of a classifier...
  • Blog Post: Embracing Uncertainty – the Role of Probabilities

    This is the first of a 2-part blog post by Chris Bishop , Distinguished Scientist at Microsoft Research. The second part was later posted here . Almost every application of machine learning (ML) involves uncertainty. For example, if we are classifying images according to the objects they contain,...
  • Blog Post: From Stumps to Trees to Forests

    This blog post is authored by Chris Burges , Principal Research Manager at Microsoft Research, Redmond. In my last post we looked at how machine learning (ML) provides us with adaptive learning systems that can solve a wide variety of industrial strength problems, using Web search as a case study...
  • Blog Post: Video - Joseph Sirosh Interview with theCUBE at BigDataNYC 2014

    Joseph Sirosh was recently interviewed in NYC by Dave Vellante and Jeff Frick on theCube . He covers a lot of ground including suggestions for aspiring data scientists, the great opportunity on the Azure marketplace and also the future of machine learning and Azure ML Check out the video below. ...
  • Blog Post: Video - Joseph Sirosh Keynote: "A New Data Science Economy" at Strata + Hadoop 2014

    Be sure to check out Joseph's keynote talk below, under 10 minutes long, summarizing how, in the emerging new Data Science Economy, data scientists are able to monetize their skills - at scale, in the cloud - just like app developers have been able to do for several years now. Joseph blogged about...
  • Blog Post: Web Services and Marketplaces Create a New Data Science Economy

    This blog post is authored by Joseph Sirosh , Corporate Vice President of Machine Learning at Microsoft. Yesterday, at Strata + Hadoop World , we announced the expansion of our data services with support of real-time analytics for Apache Hadoop in Azure HDInsight and new machine learning (ML) capabilities...
  • Blog Post: Distributed Cloud-Based Machine Learning

    This post is authored by Dhruv Mahajan , Sundararajan Sellamanickam and Keerthi Selvaraj , Researchers at Microsoft’s Cloud & Information Services Lab (CISL) and at Microsoft Research. Enterprises of all stripes are amassing huge troves of data assets, e.g. logs pertaining to user behavior...
  • Blog Post: Azure ML is Helping CMU Become More Energy Efficient

    Posted by Vinod Anantharaman , head of business strategy, Microsoft Information Management and Machine Learning (IMML). Buildings are powered by multiple systems such as heating, cooling, lighting, ventilation, security and more, each of which affect occupant comfort and energy consumption. Traditionally...
  • Blog Post: Vowpal Wabbit Modules in AzureML

    This post is authored by Sudarshan Raghunathan , Principal Development Lead for modules in the Microsoft Azure ML Studio team based in Cambridge, MA. In his blog post last month, John Langford wrote about the open source Vowpal Wabbit (VW) machine learning (ML) system. He highlighted some of the...
  • Blog Post: Video – ThyssenKrupp Uses Predictive Analytics to Give Burgeoning Cities a Lift

    This is our second post in a series on how Microsoft customers are gaining actionable insights on data by operationalizing ML at scale in the cloud. B ased on an IoT (Internet of Things) case study , this post is by Vinod Anantharaman, Head of Business Strategy at Microsoft's Information Management...
  • Blog Post: Online Learning and Sub-Linear Debugging

    This blog post is authored by Paul Mineiro , Research Software Developer at Microsoft. Online learning algorithms are a class of machine learning (ML) techniques that consume the input as a stream and adapt as they consume input. They are often used for their computational desirability, e.g., for...