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Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

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Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

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Item #: 42591119

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What Stands Out

Clear Concepts

This book demystifies complex AI concepts, providing clear explanations of interpretable machine learning models aimed at researchers and practitioners, making advanced topics accessible to a broader audience.

Practical Applications

It includes real-world case studies and examples that illustrate the application of explainable AI, enabling readers to understand the practical implications and benefits of these technologies in various industries.

Comprehensive Coverage

Covers fundamental and advanced techniques of explainable AI, ensuring a thorough understanding of both theory and practice, catering to beginners and those seeking to deepen their expertise in machine learning.

Product Details

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 & 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 ofExplainable AI-XAI Group

Publisher	Springer
Publication date	December 16, 2021
Edition	2021st
Language	English
Print length	333 pages
ISBN-10	3030833550
ISBN-13	978-3030833558
Item Weight	1.48 pounds (670 grams)
Dimensions	6.14 x 0.75 x 9.21 inches (15.6 x 1.9 x 23.4 cm)

Who Should Buy?

Data Scientists
Ideal for data scientists seeking to enhance their understanding of machine learning interpretability principles and techniques.
Regulatory Professionals
Useful for professionals in compliance and regulation, addressing guidelines for transparent AI systems and ethical standards.
AI Researchers
Benefits researchers focused on developing interpretable models, fostering innovation in transparent AI technologies.

Beginners
Not suitable for absolute beginners lacking foundational knowledge in machine learning concepts, techniques, or terminologies.

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Pros

Comprehensive coverage of topics
Accessible for beginners
Well-structured and clear explanations
Incorporates real-world examples
Up-to-date with current trends

Cons

Occasional technical jargon

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Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning