Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

John Liu

ISBN 10: 3030833585 / ISBN 13: 9783030833589

Editore: Springer International Publishing, Springer International Publishing, 2022

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Druck auf Anfrage Neuware - Printed after ordering - This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students. --Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMUThis book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning.--Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYUThis is a wonderful book! I'm pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I've seen that has up-to-date and well-rounded coverage. Thank you to the authors!--Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & BioinformaticsLiterature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyone who is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge. A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, not Elektronisches Buch with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level.Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist.Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder ofExplainable AI-XAI Group.

Codice articolo 9783030833589

Dati bibliografici

• Titolo: Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning
• Editore: Springer International Publishing, Springer International Publishing
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 3030833585
• ISBN 13: 9783030833589
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 19 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 19 millimetri di profondità
• Peso: 511 grammi

Descrizione articolo

Riassunto

This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great urce for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal urce for the courses I teach, and strongly recommend it to my students.       
--Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMU

This book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning.
--Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYU


This is a wonderful book! I’m pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I’ve seen that has up-to-date and well-rounded coverage. Thank you to the authors!
--Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics

 
Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years.