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Assistant Professor receives $518,434 to apply Machine Learning to network analysis

The University of Illinois at Urbana-Champaign - College of Engineering has awarded $518,434 to Assistant Professor Maxim Raginsky to use to apply Machine Learning techniques to network analysis to try and discover how to make networks more efficient. From the article http://csl.illinois.edu/news/raginsky-receives-career-award-apply-information-theory-machine-learning-problems “The overall design objective is to make sure that the network resources are allocated in a smart way, and each user receives only the data they need without significant waste of bandwidth or power,”  said Raginsky, a member of Illinois' electrical and computer engineering faculty. Raginsky uses ecological monitoring as an example. If someone is tracking a rare bird species in a specific habitat and wants to record how many of these birds fly in and out of the area, it would be a waste of resources to continuously stream video if what the person really wants is just the arrivals and departures of the birds. A big part of the problem is learning to detect events of interest and to reliably communicate only the data describing these events. “So I want to make sure that only the relevant information gets to those who need it, despite the fact that everyone is using the same network and the kinds of information that are relevant to one user are different than the kinds of information that are relevant to somebody else,” Raginsky said. These problems are messy and complex, and there is no hope to come up with an accurate model for all kinds of data being transmitted and received over networks because of the increasing size and complexity of both the networks and the data, Raginsky said. Machine learning offers a variety of tools for extracting predictively relevant information from observations, but to date most of the research on machine learning has not focused on the network aspect and all the resource constraints that it imposes. This project will systematically explore what is and is not possible in these types of large networks with multiple learning agents, specifically identifying the effect of bandwidth limitations, losses, delays and lack of central coordination on the performance of statistical learning algorithms, thus helping develop efficient and robust coding/decoding schemes. The NSF CAREER Award is awarded by the National Science Foundation specifically to “junior faculty members who demonstrate their roles through outstanding research and education,” according to NSF’s website. Raginsky said that because these awards are for 5-year projects, the proposals take a lot of time and effort. “You propose to research something you’re really passionate about, and presumably you want to work on this topic even if it did not get funded,” Raginsky said. “So, when I heard about my proposal being recommended for funding, of course it was a relief. I will have a good time working on this problem.” Raginsky is a member of the Decision and Control group at CSL. I think that this is a wonderful problem domain in which Machine learning can prove useful.  Machine learning is a powerful set of technologies, and we have yet to even scratch the surface of what it can do for human kind.  This goes to show you that there are other great uses besides targeted advertising systems, though that is where most of the jobs are at the moment.  Do you have ay ideas as to some practical applications of Machine learning that have yet to be tested? Please share by leaving a comment.  


Machine learning: bitly can do a lot more for you than shrink your URLs..

bitly's contributions to BigData and Machine learning Greetings to all of my fellow technologists.  I wanted to write an article to let you know about some very interesting resources that bitly has made available to developers and data lovers alike.  Just in case you've been living under a rock, bitly provides a URL shortening service.  What you may not know is they offer much more than that. Popular links can tell us a lot about the world If you have been working with machine learning and big data, I'm sure you know that access to data is extremely useful and that one of the best places to find useful data regarding what's happening on the Internet and in the world is the "Social web".  Everyone uses facebook, twitter, goggle+.  A lot of people use services like these to share with the world the subjects that they find important.  So that makes these networks extremely important regarding what people are looking at on the Internet and thus what people care about throughout the world.  Considering the fact that the same people that use these social networking tools to communicate with the world also use the bitly service to communicate the URLs of the pages that they find interesting or important, one would think that bitly's data could tell us a lot about what's going on in the world, and in real-time. Hilary Mason, remember the name A new hero of mine as an activist regarding machine learning and BigData is none other than Hilary Mason.  From her blog at http://www.hilarymason.com  Hilary Mason states "I'm the Chief Scientist at bitly, co-organizer of DataGotham, Co-Founder of HackNY, member of NYC Resistor".  Her blog post titled "Bitly Social Data APIs" describes some really interesting services that bitly offers to the public for obtaining useful information regarding links shared by bitly.  These services include how to see where in the world people are consuming a particular bitly link , how to see what is the world paying attention to right now (called "bursting phrases"), and my personal favorite, a real-time search engine which will bring back search results that are relevant based on what people are clicking on the day of the search. Hilary Mason and bitly have provided some great tools for developers that are interested in machine learning and data science.  I urge you to check out some of her talks on http://www.youtube.com . Links http://www.hilarymason.com is Hilary Mason's blog  http://www.hilarymason.com/blog/bitly-social-data-apis/ is the blog post on bitly's social data apis Youtube videos: Hilary Mason on machine learning  That about does it for this article.  Please leave a comment and tell us about someone that you admire in regards to machine learning and BigData. Thanks for reading. Buddy James    


Machine Learning: 5 examples of what it is and why you should care

Machine learning examples to make you think Hello folks, and welcome to another awe inspiring article from refactorthis.net .  This article is one that I'm very excited to present.  I'm sure you've guessed by now that the topic of this post is Machine learning.   If you don't know what machine learning is or don't care, I ask you to take a look at the fascinating examples that I've presented in this article.  You just may get inspired.   Background I'm a .NET developer and I have experience working in a myriad of different business domains.  My love affair with machine learning was brought about while I was working for an e-commerce website.  My boss had asked me to look into a new API that Google had released in beta at the time that would allow you to provide data about your customers and it would suggest products based on their shopping data.  That API is called Google prediction and you can read about the Google prediction API here.  It's been a couple of years since I was introduced to this technology and since that time I've contemplated how machine learning algorithms work and what possibilities they could unlock with the right amount of data and creativity. Since then I've done a lot of reading, and planning on ways that I can collect data to use in my journey to learn as much as I can about this new frontier that we as software developers are facing.  I was fortunate enough become the first accepted team member of the open source project called NND, or Neural Network Designer by Bragisoft.  Check back soon for an article dedicated to this wonderful open source project on machine learning and neural networks. Proceed with caution Let it be known, however, that machine learning algorithms are not for the faint of heart.  This is a very complex array of concepts and I don't plan to  try to explain them in this article.  What I will do is give a brief, simple introduction to a few of the prevalent topics that one would need to research in order to implement machine learning algorithms.  The main purpose of this article is to provide some wonderful youtube videos that provide insight into the possibilities of machine learning and it's practical applications.  Some of the videos just may blow your mind! So without further ado, let's bring forth the videos! A glimpse at the future to whet your appetite  This first video is full of commentary and stunning examples of robots that lack brains but are capable of learning by way of a design that mimics a central nervous system.  Although the video depicts the future as something to be worried about, it's still a great, non technical introduction to whet your appetite and make way for the other more specific videos that are focused on simulations and applications of machine learning. Watch a simulation of robots that learn to drive In this video, we see a software demonstration of animated robots that start with no knowledge of a driving course.  Watch what happens as the simulation is processed repeatedly, allowing the robots to learn the course with each generation.  Buckle up! Hey Darwin, what do you think of this? This next video is progressively more complex, however, the simulation clearly shows how a collection of bots act as they evolve.  The bots  can attack each other with the intent of killing and eating each other to sustain life.  They also reproduce to make baby bots.  Take a look at this awesome example of machine learning.. Virtual Darwinism take two This next video is another representation of virtual evolution by machine learning techniques.  Watch block like organisms learn to fight each other over a virtual cube that represents a block of food.   Machine learning for pattern matching and recognition This video shows an application that will render CAPTCHA verification methods obsolete.  I have a decent amount of experience working with OCR engines however, this is the most accurate recognition I've ever seen. Show and tell This concludes my article on machine learning examples.  I hope you enjoyed the article and that it has peaked your interest in machine learning.  Do you have a favorite video or application that demonstrates machine learning, neural networks, prediction algorithms, pattern matching or some other related technology?  If so, we'd love to hear about it so please leave a comment with a link. Thanks for reading, Buddy James kick it on DotNetKicks.com


About the author

My name is Buddy James.  I'm a Microsoft Certified Solutions Developer from the Nashville, TN area.  I'm a Software Engineer, an author, a blogger (http://www.refactorthis.net), a mentor, a thought leader, a technologist, a data scientist, and a husband.  I enjoy working with design patterns, data mining, c#, WPF, Silverlight, WinRT, XAML, ASP.NET, python, CouchDB, RavenDB, Hadoop, Android(MonoDroid), iOS (MonoTouch), and Machine Learning. I love technology and I love to develop software, collect data, analyze the data, and learn from the data.  When I'm not coding,  I'm determined to make a difference in the world by using data and machine learning techniques. (follow me at @budbjames).  

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