Project Management By K Nagarajan Pdf Editor

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Abstract The Internet provides developers of connected software, including web sites, applications, and devices, an unprecedented opportunity to accelerate innovation by evaluating ideas quickly and accurately using trustworthy controlled experiments (e.g., A/B tests and their generalizations). From front-end user-interface changes to backend recommendation systems and relevance algorithms, from search engines (e.g., Google, Microsoft’s Bing, Yahoo) to retailers (e.g., Amazon, eBay, Netflix, Etsy) to social networking services (e.g., Facebook, LinkedIn, Twitter) to Travel services (e.g., Expedia, Airbnb, Booking.com) to many startups, online controlled experiments are now utilized to make data-driven decisions at a wide range of companies. While the theory of a controlled experiment is simple, and dates back to Sir Ronald A. Fisher’s experiments at the Rothamsted Agricultural Experimental Station in England in the 1920s, the deployment and mining of online controlled experiments at scale (e.g., hundreds of experiments run every day at Bing) and deployment of online controlled experiments across dozens of web sites and applications has taught us many lessons. We provide an introduction, share real examples, key lessons, and cultural challenges. Bio is a Microsoft Distinguished Engineer and the General Manager for Microsoft’s Analysis and Experimentation team at Microsoft.

He joined Microsoft in 2005 and founded the team in 2006. He was previously the director of data mining and personalization at Amazon.com, and the Vice President of Business Intelligence at Blue Martini Software, which went public in 2000, and later acquired by Red Prairie. Prior to joining Blue Martini, Kohavi managed MineSet project, Silicon Graphics’ award-winning product for data mining and visualization. He joined Silicon Graphics after getting a Ph.D.

In Machine Learning from Stanford University, where he led the project, the Machine Learning library in C++ used in MineSet and at Blue Martini Software. Kohavi received his BA from the Technion, Israel. He was the General Chair for KDD 2004, co-chair of KDD 99’s industrial track with Jim Gray, and co-chair of the KDD Cup 2000 with Carla Brodley. He was an invited speaker at the National Academy of Engineering in 2000, a keynote speaker at PAKDD 2001, an invited speaker at KDD 2001’s industrial track, a keynote speaker at EC 2010 and at Recsys 2012. His papers have over citations and three of his papers are in the top 1,000 most-cited papers in Computer Science. Abstract Increasingly it is data, vast amounts of data, that drives scientific discovery. At the heart of this so-called “fourth paradigm of science” is the rapid development of large scale statistical data fusion and machine learning methods.

While these developments in “big data” methods are largely driven by commercial applications such as internet search or customer modelling, the opportunity for applying these to scientific discovery is huge. This talk will describe a number of applied machine learning projects addressing real-world inference problems in physical, life and social science areas. In particular, I will describe a major Science and Industry Endowment Fund (SIEF) project, in collaboration with the NICTA and Macquarie University, looking to apply machine learning techniques to discovery in the natural sciences. This talk will look at the key methods in machine learning that are being applied to the discovery process, especially in areas like geology, ecology and biological discovery. Bio is a Professor and ARC Federation Fellow at the University of Sydney. From 2010-2014, he was CEO of National ICT Australia (NICTA), and from 1995-2010 Director of the ARC Centre of Excellence for Autonomous Systems and of the Australian Centre for Field Robotics (ACFR). He has published over 350 research papers and founded four successful start-up companies.