Open Source Bridge

Excerpt

Description

Through the implementation of an honest-to-goodness Bayesian classifier, we’ll tour the major topics of supervised machine learning: tokenization, feature selection and vectorization, model training and tuning, and execution. Time permitting, we’ll touch on other techniques and topics.

Bring a laptop and an editor — at the end of the session, you should have your own classifier, understand how it works, and have some ideas for how to make it better.

Tags

machine learning, bayesian classifier, do-it-yourself, math

Speaker

• 

  John Melesky

  • Website: http://phaedrusdeinus.org/
  • Blog: http://blog.phaedrusdeinus.org/
  • Twitter: jmelesky

  Biography

  John’s been programming on the web since gopher was a legitimate competitor. He is an independent consultant who specializes in machine learning, natural language processing, and how those are applied to the web.

  Sessions

  • * Write your own Bayesian Classifier: An Introduction to Machine Learning
    • Title: Write your own Bayesian Classifier: An Introduction to Machine Learning
    • Track: Cooking
    • Room: Morrison
    • Time: 1:45 – 3:30pm
    • Excerpt:

      Can you perform simple arithmetic? Do you know how to program well enough to open and read files? Then you can write a Bayesian classifier, one of the machine learning techniques for predicting categories, most famous for its use in spam filters. Let’s demystify this impressively-named but ultimately simple process.

    • Speakers: John Melesky
  • * Speed up that library when you can't C a thing
    • Title: Speed up that library when you can't C a thing
    • Track: Chemistry
    • Room: Morrison
    • Time: 5:00 – 5:45pm
    • Excerpt:

      The problem: you’re using a modern dynamic language not known for speed, and you’ve identified a bottleneck. Write it in C? Does that give you the shakes? There are other language options available…

    • Speakers: John Melesky