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ISBN 10: 1009098381 ISBN 13: 9781009098380
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Published by Cambridge University Press 8/10/2023, 2023
ISBN 10: 1009098381 ISBN 13: 9781009098380
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Published by Cambridge University Press, 2023
ISBN 10: 1009098381 ISBN 13: 9781009098380
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Published by Cambridge University Press, 2023
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Published by Cambridge University Press, 2023
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Add to basketHardcover. Condition: new. Hardcover. The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future. This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications. Topics covered include the analysis of supervised learning algorithms in the iid setting, the analysis of neural networks (e.g. neural tangent kernel and mean-field analysis), and the analysis of machine learning algorithms in the sequential decision setting (e.g. online learning, bandit problems, and reinforcement learning). Students will learn the basic mathematical tools used in the theoretical analysis of these machine learning problems and how to apply them to the analysis of various concrete algorithms. This textbook is perfect for readers who have some background knowledge of basic machine learning methods, but want to gain sufficient technical knowledge to understand research papers in theoretical machine learning. This self-contained textbook introduces students and researchers of AI to the key mathematical concepts and techniques necessary to learn and analyze machine learning algorithms. Readers will gain the technical knowledge needed to understand research papers in theoretical machine learning, without much difficulty. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Published by Cambridge University Press, Cambridge, 2023
ISBN 10: 1009098381 ISBN 13: 9781009098380
Language: English
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Add to basketHardcover. Condition: new. Hardcover. The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future. This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications. Topics covered include the analysis of supervised learning algorithms in the iid setting, the analysis of neural networks (e.g. neural tangent kernel and mean-field analysis), and the analysis of machine learning algorithms in the sequential decision setting (e.g. online learning, bandit problems, and reinforcement learning). Students will learn the basic mathematical tools used in the theoretical analysis of these machine learning problems and how to apply them to the analysis of various concrete algorithms. This textbook is perfect for readers who have some background knowledge of basic machine learning methods, but want to gain sufficient technical knowledge to understand research papers in theoretical machine learning. This self-contained textbook introduces students and researchers of AI to the key mathematical concepts and techniques necessary to learn and analyze machine learning algorithms. Readers will gain the technical knowledge needed to understand research papers in theoretical machine learning, without much difficulty. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Published by Cambridge University Press, Cambridge, 2023
ISBN 10: 1009098381 ISBN 13: 9781009098380
Language: English
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Hardcover. Condition: new. Hardcover. The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future. This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications. Topics covered include the analysis of supervised learning algorithms in the iid setting, the analysis of neural networks (e.g. neural tangent kernel and mean-field analysis), and the analysis of machine learning algorithms in the sequential decision setting (e.g. online learning, bandit problems, and reinforcement learning). Students will learn the basic mathematical tools used in the theoretical analysis of these machine learning problems and how to apply them to the analysis of various concrete algorithms. This textbook is perfect for readers who have some background knowledge of basic machine learning methods, but want to gain sufficient technical knowledge to understand research papers in theoretical machine learning. This self-contained textbook introduces students and researchers of AI to the key mathematical concepts and techniques necessary to learn and analyze machine learning algorithms. Readers will gain the technical knowledge needed to understand research papers in theoretical machine learning, without much difficulty. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Published by Cambridge University Press, 2023
ISBN 10: 1009098381 ISBN 13: 9781009098380
Language: English
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Add to basketHardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 1050.