Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

This book provides a comprehensive and up-to-date review of Explainable AI methods, covering a wide range of algorithms and applications. It is written for readers entering the field as well as practitioners with a background in AI and an interest in developing real-world applications. The case studies and associated material make it an excellent resource for classroom use, and the book is highly recommended for both industry and academia.

Format: Paperback / softback
Length: 310 pages
Publication date: 17 December 2022
Publisher: Springer Nature Switzerland AG

This book is a comprehensive guide for both readers entering the field of AI and practitioners with a background in AI and an interest in developing real-world applications. It serves as a valuable resource for practitioners and researchers in both industry and academia, offering insightful case studies and associated material that can inspire a wide range of projects and hands-on assignments in a classroom setting.

The authors have designed a curriculum that introduces interpretability to machine learning at various stages, making it accessible to a diverse audience. They provide compelling examples of how core teaching practices, such as leading interpretive discussions, can be effectively taught and learned by teachers, and how sustained effort can help strengthen the quality of AI and machine learning outcomes.

The book is particularly noteworthy for its up-to-date and well-rounded coverage of explainable AI. It goes beyond mainstream algorithms like SHAP and LIME and provides a comprehensive overview of various techniques and methods, including visualizations, counterfactual analysis, and feature importance analysis. This makes it an invaluable resource for scientists, data scientists, educators, and ML developers who want to gain a deeper understanding of explainable AI and its applications.

One of the standout features of this book is its practicality. The authors have carefully crafted each chapter to be self-contained, allowing readers to follow the content at their own pace. They provide clear explanations, examples, and code snippets that help reinforce the concepts and make them easier to understand. Additionally, the book includes a wealth of references and resources that further expand the reader's knowledge on the topic.

Another impressive aspect of this book is its authorship. The authors are highly respected experts in the field of AI and machine learning, with extensive experience in both industry and academia. Their expertise and insights shine through in the text, making it a reliable and authoritative source of information.

Overall, this book is a must-read for anyone interested in developing real-world AI applications. It offers a comprehensive curriculum that covers interpretability from various perspectives, provides practical insights, and is authored by leading experts in the field. I highly recommend it to practitioners, researchers, educators, and students alike.

In conclusion, this book is a remarkable resource for anyone seeking to gain a deeper understanding of explainable AI and its applications. Its comprehensive coverage, practical approach, and authoritative authorship make it an invaluable tool for practitioners, researchers, educators, and students alike. I strongly recommend it to anyone interested in advancing the field of AI and improving the quality of its outcomes.

Weight: 516g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030833589
Edition number: 1st ed. 2021