Monday 23rd April, 2012
11:30am to 12:15pm
Many people know that machine learning techniques can facilitate learning from, and adapting to, noisy, real-world data, but aren't sure how to begin using them. Starting with two real-world examples, we will introduce you to some libraries that bring machine learning techniques to your Rails applications. We will then dive into the art of feature design, one of the first practical roadblocks that many people encounter when applying machine learning. Feature design is the challenging, subtle, and often trail-and-error process of selecting and transforming the data you provide for your learning algorithm, and it is often the hardest part of using these techniques. Our goal is for you to come out of this talk with the tools necessary to think about machine learning and how to apply it to your problems.
Experimentalist, web developer, and VP of Engineering at @Mavenlink.
Andrew Cantino has been building web applications for over fifteen years. Andrew has a Masters in Computer Science from Georgia Tech, where he focused on machine learning and artificial intelligence. He has worked on Gmail at Google, on video search at CastTV, and recently spent two years practicing Agile software development at Pivotal Labs. Andrew is currently VP of Engineering at Mavenlink.
I develop web applications and get computers to do my bidding. bio from Twitter
Ryan Stout has also been doing web development for fifteen years and has been working with Rails for the last six. He runs a small web-consulting agency and has been involved in startups ranging from social gaming to online dating and domain search. He spent the last year developing a stealth startup that uses both natural language processing systems and modern machine learning techniques.
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