| 書目名稱 | Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning | | 編輯 | Uday Kamath,John Liu | | 視頻video | http://file.papertrans.cn/320/319295/319295.mp4 | | 概述 | Single resource addressing the theory and practice of interpretability and explainability techniques using case studies.Covers exploratory data analysis, feature importance, interpretable algorithms, | | 圖書封面 |  | | 描述 | 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 A | | 出版日期 | Book 2021 | | 關(guān)鍵詞 | interpretability of models; explainability; intrinsic methods; model-agnostic methods; deep learning met | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-83356-5 | | isbn_softcover | 978-3-030-83358-9 | | isbn_ebook | 978-3-030-83356-5 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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