Machine Learning and data mining have long been seen as "black arts" - partly because they sit squarely between various rigorous disciplines (statistics, linear algebra, analysis, geometry), partly because of PhD level practitioners, and partly because any meaningful analysis required access to expensive computing clusters. This exploration will cover a basic (and gentle) introduction to machine learning, and a brief overview of what sort of problems fit well into the machine learning bag. We'll walk through a few examples (running on Engine Yard Cloud) of how this might fit into your project, and the drastically lower economics of cloud-enabled machine learning analysis.
Randall Thomas is a classically trained musician that took one too many calculus classes along the way and got sucked into the sciences. Being both blessed and cursed with a strange form of technology ADD, Randall has worked in various industries with numerous startups: everything from robotics, to low level telecommunications and networking to applied computing for stock trading systems. Randall's most recent obsession with shiny new technology comes in the form of not-quite-yet-mainstream languages like Ruby, Erlang, and OCaml.