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Explainable artificial intelligence:...

Kamath, Uday.

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Explainable artificial intelligence: an introduction to interpretable machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Explainable artificial intelligence: an introduction to interpretable machine learning/ by Uday Kamath, John Liu.
Author:	Kamath, Uday.
other author:	Liu, John.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xxiii, 310 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	1. Introduction to Interpretability and Explainability -- 2. Pre-Model Interpretability and Explainability -- 3. Model Visualization Techniques and Traditional Interpretable Algorithms -- 4. Model Interpretability: Advances in Interpretable Machine Learning -- 5. Post-hoc Interpretability and Explanations -- 6. Explainable Deep Learning -- 7. Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision -- 8. XAI: Challenges and Future.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-3-030-83356-5
ISBN:	9783030833565

Explainable artificial intelligence: an introduction to interpretable machine learning
Kamath, Uday.

Explainable artificial intelligence: an introduction to interpretable machine learning[electronic resource] /by Uday Kamath, John Liu. - Cham :Springer International Publishing :2021. - xxiii, 310 p. :ill. (chiefly col.), digital ;24 cm.

1. Introduction to Interpretability and Explainability -- 2. Pre-Model Interpretability and Explainability -- 3. Model Visualization Techniques and Traditional Interpretable Algorithms -- 4. Model Interpretability: Advances in Interpretable Machine Learning -- 5. Post-hoc Interpretability and Explanations -- 6. Explainable Deep Learning -- 7. Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision -- 8. XAI: Challenges and Future.

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, 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. 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, notebooks 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 of Explainable AI-XAI Group.

ISBN: 9783030833565

Standard No.: 10.1007/978-3-030-83356-5doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: Q335 / .K36 2021

Dewey Class. No.: 006.3

Explainable artificial intelligence: an introduction to interpretable machine learning
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505 0 $a 1. Introduction to Interpretability and Explainability -- 2. Pre-Model Interpretability and Explainability -- 3. Model Visualization Techniques and Traditional Interpretable Algorithms -- 4. Model Interpretability: Advances in Interpretable Machine Learning -- 5. Post-hoc Interpretability and Explanations -- 6. Explainable Deep Learning -- 7. Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision -- 8. XAI: Challenges and Future.
520 $a 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, 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. 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, notebooks 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 of Explainable AI-XAI Group.
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