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I've recently submitted a couple of talk proposals to upcoming conferences. Here are the abstracts.
If you read a book or take a course on machine learning, you'll probably spend a lot of time learning about how to implement standard algorithms like k-nearest neighbours or Naive Bayes. That's all very interesting, but we're Perl programmers - all that stuff's on CPAN already. This talk will focus on how to use those algorithms to attack problems, how to select the best ML algorithm for your task, and how to measure and improve the performance of your machine learning system. Code samples will be in Perl, but most of what I'll say will be applicable to machine learning in any language.
You may already know Euler's remarkable result that if a polyhedron has V vertices, E edges and F faces, then V - E + F = 2. This is a special case of the beautiful classification theorem for closed surfaces. I will state this classification theorem, and give a quick sketch of a proof.
Machine learning in (without loss of generality) Perl
London Perl Workshop, Saturday 24th November 2012. 25 minutes.If you read a book or take a course on machine learning, you'll probably spend a lot of time learning about how to implement standard algorithms like k-nearest neighbours or Naive Bayes. That's all very interesting, but we're Perl programmers - all that stuff's on CPAN already. This talk will focus on how to use those algorithms to attack problems, how to select the best ML algorithm for your task, and how to measure and improve the performance of your machine learning system. Code samples will be in Perl, but most of what I'll say will be applicable to machine learning in any language.
Classifying Surfaces
MathsJam: The Annual Conference, 17th-18th November 2012. 5 minutes.You may already know Euler's remarkable result that if a polyhedron has V vertices, E edges and F faces, then V - E + F = 2. This is a special case of the beautiful classification theorem for closed surfaces. I will state this classification theorem, and give a quick sketch of a proof.
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