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Mathematics for Machine Learning by [Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong]

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Marc Peter Deisenroth
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Mathematics for Machine Learning 1st Edition, Kindle Edition

by Marc Peter Deisenroth (Author), A. Aldo Faisal (Author), Cheng Soon Ong (Author) Format: Kindle Edition
4.6 4.6 out of 5 stars 645 ratings
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  1. ISBN-13
    978-1108470049
  2. Edition
    1st
  3. Sticky notes
    Not Enabled
  4. Publisher
    Cambridge University Press
  5. Publication date
    April 23 2020
  6. Part of series
    Studies in Natural Language Processing
  7. Language
    English
  8. File size
    17588 KB
  9. See all details
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Mathematics for Machine Learning, Cambridge University Press, linear algebra

Product description

About the Author

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 20Mio£ UKRI Center for Doctoral Training in AI for Healthcare. Faisal studied Computer Science and Physics at the Universität 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 Eidgenössische Technische Hochschule (ETH) Zürich, and in 2012 and 2013 he worked in the Diagnostic Genomics Team at NICTA in Melbourne. --This text refers to an alternate kindle_edition edition.

Review

‘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 --This text refers to an alternate kindle_edition edition.

Product details

  • ASIN ‏ : ‎ B083M7DBP6
  • Publisher ‏ : ‎ Cambridge University Press; 1st edition (April 23 2020)
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 17588 KB
  • Simultaneous device usage ‏ : ‎ Up to 4 simultaneous devices, per publisher limits
  • Text-to-Speech ‏ : ‎ Not enabled
  • Enhanced typesetting ‏ : ‎ Not Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Sticky notes ‏ : ‎ Not Enabled
  • Print length ‏ : ‎ 398 pages
  • Customer Reviews:
    4.6 4.6 out of 5 stars 645 ratings

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  1. A. Aldo Faisal

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    Professor Dr Aldo Faisal (@AnalogAldo) is Professor of Artificial Intelligence & Neuroscience at Imperial College London where is lab is based (https://FaisalLab.org). He was awarded a prestigious UKRI Turing AI Fellowship and is since 2019 the Founding Director of the UKRI Centre for Doctoral Training in AI for Healthcare (https://ai4health.io). Aldo works at the interface of AI and Biomedical Engineering to help people in disease and health. Core to his research approach is the virtuous cycle between using data-driven/AI methods to understand, model and predict from human beahviour, neuroscientific and biomedical problems, and in turn use the scientific discoveries gained from studying these problems to drive novel Machine Learning methods. He holds honorary posts at the Gatsby Computational Neuroscience Unit (UCL, London), at the MRC London Institute of Medical Sciences, or as Honorary Senior Fellow at the Nuffield Department of Clinical Neuroscience at the University of Oxford. He received a series of research prizes and distinctions such as being elected to the Global Futures Council of the World Economic Forum and winning the Toyota Mobility Foundation $50,000 Research Discovery Prize in 2018. He co-authored the Cambridge University Press textbook "Mathematics for Machine Learning" which was inspired from his experiences in pioneering courses on Machine Learning for computer science, natural sciences and engineering students long before it became popular.

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  2. Marc Peter Deisenroth

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Customer reviews

4.6 out of 5 stars
4.6 out of 5
645 global ratings
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4.0 out of 5 stars Skimming over math in part 1 made it tough for me - Great read for part 2
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5.0 out of 5 stars Very good book to learn the mathematics behind machine learning
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