Machine Learning: A Guide for the Perplexed, Part One

With the increasingly vast volumes of data generated by enterprises, relying on static rule-based decision systems is no longer competitive; instead, there is an unprecedented opportunity to optimize decisions, and adapt to changing conditions, by leveraging patterns in real-time and historical data. The very size of the data however makes it impossible for humans to find these patterns, and this has lead to an explosion of industry interest in the field of Machine Learning, which is the science and practice of designing computer algorithms that, broadly speaking, find patterns in large volumes of data. ML is particularly important in digital marketing: understanding how to leverage vast amounts of data about digital audiences and the media they consume can be the difference between success and failure for the world's largest brands. MediaMath's vision is for every addressable interaction between a marketer and a consumer to be driven by ML optimization against all available, relevant data at that moment, to maximize long-term marketer business outcomes. In this series of blog posts we will present a very basic, non-technical introduction to Machine Learning.