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Look Inside Machine Learning Fundamentals

Machine Learning Fundamentals
A Concise Introduction

$49.99 (P)

  • Publication planned for: January 2022
  • availability: Not yet published - available from January 2022
  • format: Paperback
  • isbn: 9781108940023

$ 49.99 (P)
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About the Authors
  • This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

    • Succinct rather than exhaustive, the focused presentation and coherent overview framework allows readers to understand the core ideas and learn implementations quickly
    • Over 200 color illustrations and more than 50 worked examples, case studies, lab projects in MATLAB and Python, and numerous exercises aid understanding
    • Covers recent important developments in AI and deep learning as well as traditional supervised machine learning methods
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    Reviews & endorsements

    'Dr Jiang has done a superb job in covering many methods, both theoretical and practical, across a broad spectrum of machine learning in this timely book. I worked closely with Dr Jiang on Bayesian speech recognition during late 90's and I have personally witnessed his excellent skills in applying machine learning to solving a wide range of practical problems. In this book, Dr Jiang has expanded his scope into a much wider set of logically organized topics in modern machine learning. The organization of the material is highly unique and cogent. A number of hot topics in machine learning, including deep learning and neural networks, are naturally incorporated in the book, which not only provides sufficient technical depth for the readers but also aligns well with popular toolkits for implementing the related machine learning methods.' Li Deng, formerly of Microsoft Corporation and Citadel LLC

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    Product details

    • Publication planned for: January 2022
    • format: Paperback
    • isbn: 9781108940023
    • dimensions: 253 x 203 mm
    • availability: Not yet published - available from January 2022
  • Table of Contents

    1. Introduction
    2. Mathematical Foundation
    3. Supervised Machine Learning (in a nutshell)
    4. Feature Extraction
    5. Statistical Learning Theory
    6. Linear Models
    7. Learning Discriminative Models in General
    8. Neural Networks
    9. Ensemble Learning
    10. Overview of Generative Models
    11. Unimodal Models
    12. Mixture Models
    13. Entangled Models
    14. Bayesian Learning
    15. Graphical Models.

  • Author

    Hui Jiang, York University, Toronto
    Hui Jiang is Professor of Electrical Engineering and Computer Science at York University, where he has been since 2002. His main research interests include machine learning, particularly deep learning, and its applications to speech and audio processing, natural language processing, and computer vision. Over the past 30 years, he has worked on a wide range of research problems from these areas and published hundreds of technical articles and papers in the mainstream journals and top-tier conferences. His works have won the prestigious IEEE Best Paper Award and the ACL Outstanding Paper honor.

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