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Marc Peter Deisenroth is a Senior Lecturer in Statistical Machine Learning at the Department of Computing, Imperial College London. His research interests center around data-efficient and autonomous machine learning, and he has taught courses at both Imperial College London and at the African Institute for Mathematical Sciences (Rwanda). Deisenroth was Program Chair of EWRL 2012, Workshops Chair of RSS 2013 and received Best Paper Awards at ICRA 2014 and ICCAS 2016. In 2018, Deisenroth has been awarded The President's Award for Outstanding Early Career Researcher. He is a recipient of a Google Faculty Research Award and a Microsoft Ph.D. Scholarship. A. Aldo Faisal leads the Brain and Behaviour Lab at Imperial College London, where he is also a Reader in Neurotechnology at the Department of Bioengineering and the Department of Computing. He was elected Junior Research Fellow at the University of Cambridge and has worked with Daniel Wolpert FRS on human sensorimotor control at the Computational and Biological Learning Group. Faisal worked on strategic management consulting with McKinsey & Co. and was a 'quant' with the investment bank Credit Suisse. His research aims at understanding the brain with principles from engineering, which translates into direct technological applications for patients and society. Cheng Soon Ong is Principal Research Scientist at the Machine Learning Research Group, Data61, Commonwealth Scientific and Industrial Research Organisation, Canberra (CSIRO). He is also Adjunct Associate Professor at Australian National University. His research focuses on enabling scientific discovery by extending statistical machine learning methods. Ong received his Ph.D. in Computer Science at Australian National University in 2005. He was a postdoc at Max Planck Institute of Biological Cybernetics and Fredrich Miescher Laboratory. From 2008 to 2011, he was a lecturer in the Department of Computer Science at Eidgenoessische Technische Hochschule Zurich, and in 2012 and 2013 he worked in the Diagnostic Genomics Team at NICTA in Melbourne.
'This book provides great coverage of all the basic mathematical concepts for machine learning. I'm looking forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals.' Joelle Pineau, McGill University, Montreal 'The field of machine learning has grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory. It will prove valuable both as a tutorial for newcomers to the field, and as a reference text for machine learning researchers and engineers.' Christopher Bishop, Microsoft Research Cambridge 'This book provides a beautiful exposition of the mathematics underpinning modern machine learning. Highly recommended for anyone wanting a one-stop-shop to acquire a deep understanding of machine learning foundations.' Pieter Abbeel, University of California, Berkeley 'Really successful are the numerous explanatory illustrations, which help to explain even difficult concepts in a catchy way. Each chapter concludes with many instructive exercises. An outstanding feature of this book is the additional material presented on the website …' Volker H. Schulz, SIAM Review
Hot Molecules, Cold Electrons
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