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Mathematics for Machine Learning
By: A. Aldo Faisal, Marc Peter Deisenroth, Cheng Soon Ong
Paperback | 23 April 2020
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This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
About the Authors
Marc Peter Deisenroth is DeepMind Chair in Artificial Intelligence at the Department of Computer Science, University College London. Prior to this, he was a faculty member in the Department of Computing, Imperial College London. His research areas include data-efficient learning, probabilistic modeling, and autonomous decision making. Deisenroth was Program Chair of the European Workshop on Reinforcement Learning (EWRL) 2012 and Workshops Chair of Robotics Science and Systems (RSS) 2013. His research received Best Paper Awards at the International Conference on Robotics and Automation (ICRA) 2014 and the International Conference on Control, Automation and Systems (ICCAS) 2016. In 2018, he was awarded the President's Award for Outstanding Early Career Researcher at Imperial College London. He is a recipient of a Google Faculty Research Award and a Microsoft P.hD. grant.
A. Aldo Faisal leads the Brain and Behaviour Lab at Imperial College London, where he is faculty at the Departments of Bioengineering and Computing and a Fellow of the Data Science Institute. He is the director of the 20MioGBP UKRI Center for Doctoral Training in AI for Healthcare. Faisal studied Computer Science and Physics at the Universitat Bielefeld (Germany). He obtained a Ph.D. in Computational Neuroscience at the University of Cambridge and became Junior Research Fellow in the Computational and Biological Learning Lab. His research is at the interface of neuroscience and machine learning to understand and reverse engineer brains and behavior.
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 Friedrich Miescher Laboratory. From 2008 to 2011, he was a lecturer in the Department of Computer Science at Eidgenoessische Technische Hochschule (ETH) Zurich, and in 2012 and 2013 he worked in the Diagnostic Genomics Team at NICTA in Melbourne.
Industry Reviews
'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
ISBN: 9781108455145
ISBN-10: 110845514X
Published: 23rd April 2020
Format: Paperback
Language: English
Number of Pages: 390
Audience: General Adult
Publisher: Cambridge University Press
Country of Publication: GB
Dimensions (cm): 29.9 x 19.7 x 4.7
Weight (kg): 0.82
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This product is categorised by
- Non-FictionComputing & I.T.Computer ScienceArtificial IntelligenceComputer Vision
- Non-FictionComputing & I.T.Computer ScienceArtificial IntelligenceMachine Learning
- Non-FictionEngineering & TechnologyTechnology in GeneralMaths for Engineers
- Non-FictionMathematicsProbability & Statistics
- Non-FictionComputing & I.T.Computer ScienceArtificial IntelligencePattern Recognition
- Text BooksHigher Education & Vocational TextbooksEngineering & Physics Higher Education Textbooks