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Editore: Springer International Publishing AG, Cham, 2024
ISBN 10: 3031532813 ISBN 13: 9783031532818
Lingua: Inglese
Da: Grand Eagle Retail, Mason, OH, U.S.A.
EUR 69,13
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories:1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of probability and statistics as well as its relationship to machine learning. The fundamentals of probability and statistics are covered in Chapters 2 through 5.2. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner. Chapters 6 through 9 explore how different models from probability and statistics are applied to machine learning. Perhaps the most important tool that bridges the gap from data to probability is maximum-likelihood estimation, which is a foundational concept from the perspective of machine learning. This concept is explored repeatedly in these chapters.3. Advanced topics: Chapter 10 is devoted to discrete-state Markov processes. It explores the application of probability and statistics to a temporal and sequential setting, although the applications extend to more complex settings such as graphical data. Chapter 11 covers a number of probabilistic inequalities and approximations.The style of writing promotes the learning of probability and statistics simultaneously with a probabilistic perspective on the modeling of machine learning applications. The book contains over 200 worked examples in order to elucidate key concepts. Exercises are included both within the text of the chapters and at the end of the chapters. The book is written for a broad audience, including graduate students, researchers, and practitioners. This book covers probability and statistics from the machine learning perspective. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 77,45
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Editore: Springer International Publishing AG, CH, 2024
ISBN 10: 3031532813 ISBN 13: 9783031532818
Lingua: Inglese
Da: Rarewaves USA, OSWEGO, IL, U.S.A.
EUR 97,96
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 85,47
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Aggiungi al carrellohardcover. Condizione: Sehr gut. 540 Seiten; 9783031532818.2 Gewicht in Gramm: 2.
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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Editore: Springer-Nature New York Inc, 2024
ISBN 10: 3031532813 ISBN 13: 9783031532818
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 106,40
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Editore: Springer International Publishing AG, CH, 2024
ISBN 10: 3031532813 ISBN 13: 9783031532818
Lingua: Inglese
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 135,85
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Editore: Springer Nature Switzerland, Springer Nature Switzerland, 2025
ISBN 10: 3031532848 ISBN 13: 9783031532849
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 69,54
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering.
Editore: Springer International Publishing AG, CH, 2024
ISBN 10: 3031532813 ISBN 13: 9783031532818
Lingua: Inglese
Da: Rarewaves USA United, OSWEGO, IL, U.S.A.
EUR 100,22
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Aggiungi al carrelloHardback. Condizione: New. 2024 ed.
Editore: Springer Nature Switzerland, Springer International Publishing Mai 2024, 2024
ISBN 10: 3031532813 ISBN 13: 9783031532818
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 96,29
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware -This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories:1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of probability and statistics as well as its relationship to machine learning. The fundamentals of probability and statistics are covered in Chapters 2 through 5.2. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner. Chapters 6 through 9 explore how different models from probability and statistics are applied to machine learning. Perhaps the most important tool that bridges the gap from data to probability is maximum-likelihood estimation, which is a foundational concept from the perspective of machine learning. This concept is explored repeatedly in these chapters.3. Advanced topics: Chapter 10 is devoted to discrete-state Markov processes. It explores the application of probability and statistics to a temporal and sequential setting, although the applications extend to more complex settings such as graphical data. Chapter 11 covers a number of probabilistic inequalities and approximations.The style of writing promotes the learning of probability and statistics simultaneously with a probabilistic perspective on the modeling of machine learning applications. The book contains over 200 worked examples in order to elucidate key concepts. Exercises are included both within the text of the chapters and at the end of the chapters. The book is written for a broad audience, including graduate students, researchers, and practitioners.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 540 pp. Englisch.
Editore: Springer International Publishing AG, Cham, 2024
ISBN 10: 3031532813 ISBN 13: 9783031532818
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 148,38
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories:1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of probability and statistics as well as its relationship to machine learning. The fundamentals of probability and statistics are covered in Chapters 2 through 5.2. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner. Chapters 6 through 9 explore how different models from probability and statistics are applied to machine learning. Perhaps the most important tool that bridges the gap from data to probability is maximum-likelihood estimation, which is a foundational concept from the perspective of machine learning. This concept is explored repeatedly in these chapters.3. Advanced topics: Chapter 10 is devoted to discrete-state Markov processes. It explores the application of probability and statistics to a temporal and sequential setting, although the applications extend to more complex settings such as graphical data. Chapter 11 covers a number of probabilistic inequalities and approximations.The style of writing promotes the learning of probability and statistics simultaneously with a probabilistic perspective on the modeling of machine learning applications. The book contains over 200 worked examples in order to elucidate key concepts. Exercises are included both within the text of the chapters and at the end of the chapters. The book is written for a broad audience, including graduate students, researchers, and practitioners. This book covers probability and statistics from the machine learning perspective. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Editore: Springer International Publishing AG, CH, 2024
ISBN 10: 3031532813 ISBN 13: 9783031532818
Lingua: Inglese
Da: Rarewaves.com UK, London, Regno Unito
EUR 127,16
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Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 58,23
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Editore: Springer Nature Switzerland, Springer Nature Switzerland Mai 2025, 2025
ISBN 10: 3031532848 ISBN 13: 9783031532849
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 69,54
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 540 pp. Englisch.
Editore: Springer, Berlin, Springer Nature Switzerland, Springer, 2024
ISBN 10: 3031532813 ISBN 13: 9783031532818
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 96,29
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book covers probability and statistics from the machine learning perspective. Thechapters of this book belong to three categories:1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of probability and statistics as well as its relationship to machine learning. The fundamentals of probability and statistics are covered in Chapters 2 through 5.2. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner. Chapters 6 through 9 explore how different models from probability and statistics are applied to machine learning. Perhaps the most important tool that bridges the gap from data to probability is maximum-likelihood estimation, which is a foundational concept from the perspective of machine learning. This concept is explored repeatedly in these chapters.3. Advanced topics: Chapter 10 is devoted to discrete-state Markov processes. It explores the application of probability and statistics to a temporal and sequential setting, although the applications extend to more complex settings such as graphical data. Chapter 11 covers a number of probabilistic inequalities and approximations.The style of writing promotes the learning of probability and statistics simultaneously witha probabilistic perspective on the modeling of machine learning applications. The book contains over 200 worked examples in order to elucidate key concepts. Exercises are included both within the text of the chapters and at the end of the chapters. The book is written for a broad audience, including graduate students, researchers, and practitioners. 522 pp. Englisch.
Da: Majestic Books, Hounslow, Regno Unito
EUR 115,96
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Editore: Springer Nature Switzerland, Springer Nature Switzerland Mai 2025, 2025
ISBN 10: 3031532848 ISBN 13: 9783031532849
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 69,54
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories:Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 540 pp. Englisch.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 120,28
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Editore: Springer Nature Switzerland, 2024
ISBN 10: 3031532813 ISBN 13: 9783031532818
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 81,44
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Aggiungi al carrelloGebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Simple and intuitive discussions of probability and statisticsDiscusses details of applications of mathematical concepts to machine learningProvides mathematical details without losing the reader in complexityCharu C. Aggarwal is a .
Da: Majestic Books, Hounslow, Regno Unito
EUR 132,99
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