Talking head
Golden Gate Ruby Conference 2010

This presentation, by Ilya Grigorik, is licensed under a Creative Commons Attribution ShareAlike 3.0
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Machine learning is a discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data — a fancy name for a simple concept. Behind all the buzzword algorithms such as Decision Trees, Singular Value Decomposition, Bayes and Support Vector Machines lie the simple observations and principles that make them tick. In this presentation, we will take a ground-up look at how they work (in practical terms), why they work, and how you can apply them in Ruby for fun and profit. No prior knowledge required. We will take a quick look at the foundations (representing and modeling knowledge, compression, and inference), and build up to simple but powerful examples such as clustering, recommendations, and classification — all in 30 minutes or less, believe it or not.

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