Real-time Learning for Fun and Profit

I will describe how real-time Bayesian learning can optimize real processes. These processes can be web-sites, user interfaces, ad-targeting servers, back-end server farms, search engines or physical systems. Google, Microsoft and Yahoo have all adopted the algorithms described in this talk for ad targeting because they produce superior results, but you don't have to be on that scale to benefit as well.

The positive impact of these new learning techniques can be massive. Conventional techniques are harder to implement and make it harder for the consumers of the results of such tests to understand the results and to take correct actions. Worst of all, conventional techniques waste enormous amounts of precious user data making it harder to react quickly.

Counter-intuitively, while Bayesian techniques for real-time learning are based from complex mathematical theories that have only recently been fully understood, these techniques are actually very simple conceptually, are much easier to implement correctly than conventional statistical approaches and produce results that are much easier to understand, especially for non-statisticians.

In this talk, the audience will learn the basic ideas behind effective real-time learning, but also see detailed implementation techniques and learn how to architect effective testing systems. I will also cover methods for starting gently without massive system upheavals and how to build consensus around how real-time learning and optimization. The focus throughout the talk will be on practical methods that can be applied in real life.

The code described in this talk will be made freely available.

Slides: 

buzz-words-dunning-real-time-learning.pdf