Probability Statistics Machine Learning by Aggarwal Charu (29 results)

Condition: New.

Hardcover. Condition: 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.

Condition: New.

Condition: As New. Unread book in perfect condition.

Hardback. Condition: New. 2024 ed.

Condition: As New. Unread book in perfect condition.

Condition: New.

hardcover. Condition: Sehr gut. 540 Seiten; 9783031532818.2 Gewicht in Gramm: 2.

Condition: New.

Condition: New. In.

Condition: New.

Condition: New. 2024th edition NO-PA16APR2015-KAP.

Hardcover. Condition: Brand New. 540 pages. 10.00x7.01x10.00 inches. In Stock.

Hardback. Condition: New. 2024 ed.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering.

Hardcover. Condition: 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 UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardback. Condition: New. 2024 ed.

Buch. Condition: 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.

Hardcover. Condition: 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.

Hardback. Condition: New. 2024 ed.

Condition: new. Questo è un articolo print on demand.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 540 pp. Englisch.

Condition: New. Print on Demand.

Buch. Condition: 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.

Taschenbuch. Condition: 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.

Condition: New. PRINT ON DEMAND.

Gebunden. Condition: 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 .

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.