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A. Gordon (Microsoft Research)

A. Gordon (Microsoft Research)
Mon, 25. May 2015, 15:00 h
Sala seminari Ovest - Dipartimento di Informatica
fACTORY seminars


Probabilistic Programming in Tabular

A. Gordon - Microsoft Research Cambridge

Probabilistic programming is a declarative form of machine learning: the user writes a probabilistic model of their data as a short piece of code, while the compiler turns the code into an efficient inference routine to learn and predict properties of the data. A remarkably wide range of machine learning tasks - regression, classification, ranking, recommendation, and so on - can all be expressed as probabilistic programs. The hope of the field is that practical programmers without PhDs in machine learning can get to grips with probabilistic models by writing and running pieces of probabilistic code. The purpose of this tutorial is to introduce the area from first principles, and show how to develop a series of models of data from scratch. Running examples are all based on the Tabular system from Microsoft Research. No previous experience with machine learning or probabilistic models is assumed. Come to this tutorial to understand how many machine learning problems can be coded as probabilistic functional programs.

Andy Gordon @AndrewDGordon is a Principal Researcher and the Researcher Area Leader for Programming Principles and Tools at Microsoft Research Cambridge, and is a Professor at the University of Edinburgh. 

He has worked on a range of topics in concurrency, verification, and security, never straying too far from his roots in functional programming.  As a PhD student he invented Haskell’s notation “>>=” for monads. His work on ambients (with Cardelli), a theory of mobile software and hardware, has been highly cited and influential, particularly in systems biology. His spi-calculus (with Abadi) is a pioneering work in language-based security and is a basis for popular tools such as Blanchet’s ProVerif.

Since joining Microsoft in 1997 he has initiated and led a series of successful research projects and technology transfers, with impact on the .NET Common Language Runtime. In the past few years, he has embarked on an interdisciplinary project between the programming languages and machine learning groups at Microsoft Research Cambridge.

One outcome is that his probabilistic modelling language Infer.NET Fun is now a component of the machine learning group’s Infer.NET software. His current passion is Tabular, a probabilistic language for machine learning within Excel.


Sala seminari Ovest