Icrea

How can we teach machine learning to identify causal patterns in data? This book explores the very notion of “causality”, identifying from a naturalistic and evolutionary perspective how living systems deal with causal relationships. At the same time, using this knowledge to identify the best ways to apply such biological models in machine learning scenarios.

by Christopher J. Bishop (Author), Yuval Peres (Author)

How can we teach machine learning to identify causal patterns in data? This book explores the very notion of “causality”, identifying from a naturalistic and evolutionary perspective how living systems deal with causal relationships. At the same time, using this knowledge to identify the best ways to apply such biological models in machine learning scenarios.

How can we teach machine learning to identify causal patterns in data? This book explores the very notion of “causality”, identifying from a naturalistic and evolutionary perspective how living systems deal with causal relationships. At the same time, using this knowledge to identify the best ways to apply such biological models in machine learning scenarios.

“A critical and novel contribution from top climate social scientists examining the potential and contradictions of climate urbanism for an environmentally just and transformative development.”
- Isabelle Anguelovski, ICREA Research Professor and Director, Barcelona Lab for Urban Environmental Justice and Sustainability, Universitat Autònoma de Barcelona, Spain