Description: Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning by Uday Kamath, John Liu 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! Im pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book Ive 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 FORMAT Paperback LANGUAGE English CONDITION Brand New Back Cover 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! Im pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book Ive 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, Duke University. 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 Author Biography Uday Kamath has spent more than two decades developing analytics products in statistics, optimization, machine learning, NLP and speech recognition, and explainable AI. Uday has a Ph.D. in scalable machine learning and has contributed to many journals, conferences, and books in the field of AI. He is the author of books such as Deep Learning for NLP and Speech Recognition, Mastering Java Machine Learning, and Machine Learning: End-to-End Guide for Java Developers. He held many senior roles: Chief Analytics Officer for Digital Reasoning, Advisor for Falkonry, and Chief Data Scientist for BAE Systems Applied Intelligence. He has built products and solutions using AI in surveillance, compliance, cybersecurity, financial crime, anti-money laundering, and insurance fraud. Uday currently works as the Chief Analytics Officer for Smarsh. He is responsible for Data Science, research of analytics products employing deep learning and explainable AI, and modern techniques in speech and text used in the financial domain and healthcare.John Chih Liu, PhD, CFA is Chief Executive Officer of Intelluron Corporation. Previously, he held senior executive roles overseeing quantitative research, portfolio management and data science organizations, including as VP of Data Science, Applied Machine Learning at Digital Reasoning Systems, MD of Equity Strategies at the Vanderbilt University endowment, and Head of Index Options Trading at BNP Paribas. He is a frequent speaker and published author on topics including natural language processing, reinforcement learning, asset allocation, systemic risk and EM theory. John was named Nashvilles Data Scientist of the Year in 2016, Finalist for Community Leader of the Year in 2018, and Finalist for Innovator of the Year in 2020. He earned his B.S., M.S., and Ph.D. in electrical engineering from the University of Pennsylvania and is a CFA Charterholder, advocate for the global data science community and supporter of the International Science and Engineering Fair. Table of Contents 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. Review 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 AIand 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, NYULiterature 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 ofinformation 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 GroupThis is a wonderful book! Im pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book Ive 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 Review Quote 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 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 This is a wonderful book! Im pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book Ive 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 Feature Single resource addressing the theory and practice of interpretability and explainability techniques using case studies Covers exploratory data analysis, feature importance, interpretable algorithms, and model agnostic techniques Includes over 220 color figures, tables and charts, and Git repos with data and Google colab notebooks for each chapter Details ISBN3030833585 Author John Liu Pages 310 Language English Year 2022 ISBN-10 3030833585 ISBN-13 9783030833589 Format Paperback Publication Date 2022-12-17 Publisher Springer Nature Switzerland AG Edition 1st Imprint Springer Nature Switzerland AG Place of Publication Cham Country of Publication Switzerland Illustrations 161 Illustrations, color; 33 Illustrations, black and white; XXIII, 310 p. 194 illus., 161 illus. in color. UK Release Date 2022-12-17 Edition Description 1st ed. 2021 Alternative 9783030833558 DEWEY 006.3 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:139410611;
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Book Title: Explainable Artificial Intelligence: An Introduction to Interpret
