Machine Learning Fundamentals: A Concise Introduction

Author:   Hui Jiang (York University, Toronto)
Publisher:   Cambridge University Press
Edition:   New edition
ISBN:  

9781108837040


Pages:   420
Publication Date:   25 November 2021
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Machine Learning Fundamentals: A Concise Introduction


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Full Product Details

Author:   Hui Jiang (York University, Toronto)
Publisher:   Cambridge University Press
Imprint:   Cambridge University Press
Edition:   New edition
Dimensions:   Width: 20.70cm , Height: 2.70cm , Length: 25.90cm
Weight:   1.060kg
ISBN:  

9781108837040


ISBN 10:   1108837042
Pages:   420
Publication Date:   25 November 2021
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Tertiary & Higher Education
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Reviews

'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


'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 'It is beautifully designed, with many color images that make the complex subject matter manageable ... It is a book for students and developers who are committed to specializing in ML or a specific area of it.' Karl van Heijster , De Leesclub van Alles


Author Information

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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