John Langford discusses how to use Vowpal Wabbit in and as a machine learning system including architecture, unique capabilities, and applications, applied to personalized news recommendation.
Kathryn S. McKinley discusses research approaches and results that abstract, choose, and exploit hardware heterogeneity providing computational power at low energy consumption levels.
Andy Gordon discusses machine learning using functional programming, explaining how Infer.NET Fun turns the succinct syntax of F# into an executable modeling language for Bayesian machine learning.
Matthew Moloney shares some of the F# tools built at Microsoft Research for dealing with Big Data.
K. Rustan M. Leino advocates developing in stages by using languages that offer both design level abstractions – types, contracts, higher-level constructs, ghost constructs- and implementation ones.