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Statistical and causal approaches to machine learning

25.11.2014 - (idw) Max-Planck-Institut für Intelligente Systeme

2014 Milner Award Lecture by Professor Bernhard Schölkopf

The talk will introduce the basic ideas of machine learning, and illustrate them with application examples. It argues that while machine learning and "big data" analysis currently mainly focuses on statistics; the causal point of view can provide additional insights. In machine learning, we use data to automatically find dependences in the world, with the goal of predicting future observations. Most machine learning methods build on statistics, but one can also try to go beyond this, assaying causal structures underlying statistical dependences. The hope is that this also allows prediction in certain situations where systems change, for instance by interventions.

The Royal Society Milner Award, supported by Microsoft Research, is given annually for outstanding achievement in computer science by a European researcher.

Professor Bernhard Schölkopf is based at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He was given the 2014 Royal Society Milner Award in recognition of his pioneering work in machine learning which defined the field of kernel machines, now widely used in all areas of science and industry.

Thursday 27 November 2014
6:30 pm 7:30 pm

at The Royal Society, London
6-9 Carlton House Terrace
London SW1Y 5AG
+44 (0)20 7451 2500

This event is free to attend and open to all. No tickets are required. Doors open at 6pm and seats will be allocated on a first-come-first-served basis. Weitere Informationen:
uniprotokolle > Nachrichten > Statistical and causal approaches to machine learning
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