Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning
暫譯: 可解釋的人工智慧：可解釋機器學習入門
Kamath, Uday, Liu, John
- 出版商: Springer
- 出版日期: 2022-12-17
- 售價: $6,400
- 貴賓價: 9.5 折 $6,080
- 語言: 英文
- 頁數: 310
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3030833585
- ISBN-13: 9783030833589
-
相關分類:
人工智慧、Machine Learning
海外代購書籍(需單獨結帳)
商品描述
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 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
商品描述(中文翻譯)
這本書是為進入這個領域的讀者以及具有人工智慧背景並對開發實際應用感興趣的從業者所撰寫的。這本書對於業界和學術界的從業者和研究人員來說都是一個很好的資源，書中討論的案例研究和相關材料可以作為各種專案和課堂作業的靈感。我一定會將這本書作為我教學課程的個人資源，並強烈推薦給我的學生。
--Dr. Carlotta Domeniconi，喬治梅森大學計算機科學系副教授
這本書提供了一個在每個階段引入機器學習可解釋性的課程。作者提供了引人注目的例子，顯示像引導解釋性討論這樣的核心教學實踐可以由教師教授和學習，並需要持續的努力。還有什麼比這更能加強人工智慧和機器學習成果的質量呢？我希望這本書能成為教師、數據科學教育者和機器學習開發者的入門書，讓我們一起實踐可解釋機器學習的藝術。
--Anusha Dandapani，聯合國國際計算中心首席數據與分析官，紐約大學兼任教員
這是一本精彩的書！我很高興下一代科學家終於能夠學習這個重要的主題。這是我見過的第一本涵蓋最新且全面的書籍。感謝作者們！
--Dr. Cynthia Rudin，計算機科學、電氣與計算機工程、統計科學、生物統計學與生物資訊學教授
至今，關於可解釋人工智慧的文獻相對稀少，主要集中在主流算法如SHAP和LIME上。這本書填補了這一空白，提供了過去5-10年科學界提出的各種算法的極為廣泛的回顧。這本書對於任何新進入可解釋人工智慧領域的人，或已經熟悉該領域並希望擴展知識的人來說都是一個很好的指南。從可視化、可解釋方法、本地和全局解釋、時間序列方法開始，並以深度學習結束的最先進可解釋人工智慧方法的全面回顧，提供了目前在其他地方無法獲得的無與倫比的信息來源。此外，帶有生動示例的筆記本是很好的補充，使這本書對任何級別的從業者更具吸引力。
總體而言，作者為讀者提供了廣泛的涵蓋面，卻不失去對實際方面的關注，這使得這本書真正獨特，並成為任何數據科學家圖書館中的一個極佳補充。
--Dr. Andrey Sharapov，產品數據科學家、可解釋人工智慧專家及演講者、可解釋人工智慧-XAI小組創始人
作者簡介
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 Nashville's 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.
作者簡介(中文翻譯)
烏代·卡馬斯在統計學、優化、機器學習、自然語言處理（NLP）和語音識別以及可解釋的人工智慧（AI）領域擁有超過二十年的分析產品開發經驗。烏代擁有可擴展機器學習的博士學位，並在人工智慧領域的多本期刊、會議和書籍中做出了貢獻。他是《深度學習於自然語言處理與語音識別》（Deep Learning for NLP and Speech Recognition）、《精通Java機器學習》（Mastering Java Machine Learning）和《機器學習：Java開發者的端到端指南》（Machine Learning: End-to-End Guide for Java Developers）等書籍的作者。他曾擔任多個高級職位，包括數位推理公司的首席分析官、Falkonry的顧問，以及BAE系統應用智能的首席數據科學家。他利用人工智慧在監控、合規性、網絡安全、金融犯罪、反洗錢和保險詐騙等領域構建產品和解決方案。烏代目前擔任Smarsh的首席分析官，負責數據科學、使用深度學習和可解釋的人工智慧的分析產品研究，以及在金融領域和醫療保健中使用的現代語音和文本技術。
劉志誠博士，CFA是Intelluron Corporation的首席執行官。此前，他擔任高級執行職位，負責量化研究、投資組合管理和數據科學組織，包括數位推理系統的應用機器學習數據科學副總裁、范德堡大學基金的股票策略董事總經理，以及法國巴黎銀行的指數期權交易主管。他經常在自然語言處理、強化學習、資產配置、系統性風險和新興市場理論等主題上發表演講並出版著作。劉博士在2016年被評選為納什維爾年度數據科學家，2018年成為年度社區領袖的決賽入圍者，2020年則是年度創新者的決賽入圍者。他在賓夕法尼亞大學獲得電機工程的學士、碩士和博士學位，並且是CFA特許持有人，全球數據科學社群的倡導者，以及國際科學與工程博覽會的支持者。
