Human and Machine Learning (Hardback Book)
Explore the intersection of human and machine learning with this comprehensive hardcover book. Understand the 'black-box' nature of ML and discover how to achieve transparency, explainability, and trustworthiness in AI systems. This first edition delves into visualization techniques, algorithmic explanations, human cognitive responses, and the integration of domain knowledge for more informed and reliable decision-making. Ideal for researchers and practitioners in artificial intelligence, decision support systems, and human-computer interaction, this book will inspire the development of human-centered ML algorithms and empower users to confidently leverage ML outputs. Key topics covered include: - Transparency in machine learning - Visual explanation of ML processes - Algorithmic explanation of ML models - Human cognitive responses in ML-based decision making - Human evaluation of machine learning - Domain knowledge in transparent ML applications Product Summary: Human and Machine Learning, Hardcover, English, First edition, 482 pages, by Jianlong Zhou and Fang Chen. ISBN: 9783319904023.
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Details:Human and Machine Learning (Hardback Book)
Explore the intersection of human and machine learning with this comprehensive hardcover book. Understand the 'black-box' nature of ML and discover how to achieve transparency, explainability, and trustworthiness in AI systems. This first edition delves into visualization techniques, algorithmic explanations, human cognitive responses, and the integration of domain knowledge for more informed and reliable decision-making. Ideal for researchers and practitioners in artificial intelligence, decision support systems, and human-computer interaction, this book will inspire the development of human-centered ML algorithms and empower users to confidently leverage ML outputs. Key topics covered include: - Transparency in machine learning - Visual explanation of ML processes - Algorithmic explanation of ML models - Human cognitive responses in ML-based decision making - Human evaluation of machine learning - Domain knowledge in transparent ML applications Product Summary: Human and Machine Learning, Hardcover, English, First edition, 482 pages, by Jianlong Zhou and Fang Chen. ISBN: 9783319904023.
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Product Description
Explore the intersection of human and machine learning with this comprehensive hardcover book. Understand the 'black-box' nature of ML and discover how to achieve transparency, explainability, and trustworthiness in AI systems. This first edition delves into visualization techniques, algorithmic explanations, human cognitive responses, and the integration of domain knowledge for more informed and reliable decision-making. Ideal for researchers and practitioners in artificial intelligence, decision support systems, and human-computer interaction, this book will inspire the development of human-centered ML algorithms and empower users to confidently leverage ML outputs. Key topics covered include: - Transparency in machine learning - Visual explanation of ML processes - Algorithmic explanation of ML models - Human cognitive responses in ML-based decision making - Human evaluation of machine learning - Domain knowledge in transparent ML applications Product Summary: Human and Machine Learning, Hardcover, English, First edition, 482 pages, by Jianlong Zhou and Fang Chen. ISBN: 9783319904023.
Product Specifications
General | |
|---|---|
format | hardback |
Language | eng |
type | not available |
series | human-computer interaction ser. |
Brand | Springer International Publishing AG |
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