Statistics and Data Foundations for AI
暫譯: 人工智慧的統計與數據基礎
Dasu, Tamraparni, Murthy, Geetha
- 出版商: CRC
- 出版日期: 2026-07-24
- 售價: $5,630
- 貴賓價: 9.5 折 $5,348
- 語言: 英文
- 頁數: 250
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 104100642X
- ISBN-13: 9781041006428
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相關分類:
Data-mining
海外代購書籍(需單獨結帳)
商品描述
Statistics and Data Foundations for AI is an interdisciplinary approach to statistical concepts and data foundations of AI with real-world illustrative examples from authoritative sources such as NASA, NOAA and the United States Census Bureau. Co-authored by a data science research expert and an experienced educator, the book serves as a prequel to an AI and machine learning course.
Given the interdependence of data and AI, understanding data and using it responsibly to create and interact with AI tools requires a high level of statistical skill and data intuition. The book includes topics such as data management, exploratory data analysis, sampling, probability theory, hypothesis testing, multivariate analysis, data quality, ethics, data privacy, and responsible use of AI. Every key statistical concept is presented in the context of how it is used by AI applications in areas such as sports, fashion, climate science, environmental science, health, medicine, and space exploration. The book makes AI relatable to everyday life so that it is no longer an abstraction. Instructor resources, supplementary materials, further reading, and debate topics enable advanced study and deeper thinking.
Statistics and Data Foundations for AI is intended for undergraduate and graduate students, and practitioners interested in learning statistical foundations in relation to data and AI with application to real-world problems. The content is accessible to learners from a wide variety of backgrounds (STEM and non-STEM) without sacrificing rigor.
商品描述(中文翻譯)
《人工智慧的統計與數據基礎》是一種跨學科的方法，探討人工智慧的統計概念和數據基礎，並提供來自NASA、NOAA和美國人口普查局等權威來源的實際案例。這本書由一位數據科學研究專家和一位經驗豐富的教育者共同撰寫，作為人工智慧和機器學習課程的前導書籍。
考慮到數據與人工智慧之間的相互依賴，理解數據並負責任地使用它來創建和互動人工智慧工具需要高水平的統計技能和數據直覺。本書涵蓋的主題包括數據管理、探索性數據分析、抽樣、概率論、假設檢驗、多變量分析、數據質量、倫理、數據隱私以及人工智慧的負責任使用。每個關鍵的統計概念都在人工智慧應用於體育、時尚、氣候科學、環境科學、健康、醫學和太空探索等領域的背景下進行介紹。本書使人工智慧與日常生活相關聯，讓它不再是一個抽象的概念。教師資源、補充材料、進一步閱讀和辯論主題使得進階學習和深入思考成為可能。
《人工智慧的統計與數據基礎》旨在為本科生和研究生以及對學習與數據和人工智慧相關的統計基礎感興趣的從業者提供內容，並應用於現實世界的問題。該內容對來自各種背景（STEM和非STEM）的學習者都具有可及性，且不會犧牲嚴謹性。
作者簡介
Dr. Tamraparni Dasu (Ph.D. Mathematical Statistics, University of Rochester, 1991) is a research scientist and Data Science expert specializing in computational statistics, machine learning and data quality. She retired as Lead Inventive Scientist after 31 years at AT&T Bell Laboratories and now teaches Data Mining, Machine Learning and AI as an adjunct professor at Fairleigh Dickinson University, New Jersey. Dr. Dasu has published extensively in top-tier journals and research conferences such as SIGMOD, KDD and VLDB and authored the field's first technical book on data quality, Exploratory Data Mining and Data Cleaning, John Wiley (2003), with Dr. Theodore Johnson. As an educator, Dr. Dasu is committed to mentoring the next generation of quantitative thinkers, computer scientists and data scientists.
Dr. Geetha Murthy (Ed.D. Instructional Leadership, St. John's University, Queens, New York, 2015) is a highly experienced educator and administrator whose research focused on describing and dismantling self-limiting beliefs in students that inhibit them from pursuing STEM education/careers. Most recently, Dr. Murthy served as the K-12 director of mathematics for Herricks School District in Long Island, NY, a high-performing public school district. Under her leadership, the district made significant progress in increasing student achievement through initiatives that focused on systemic changes to create and sustain greater equity, access and success for all students. Dr. Murthy is passionate about promoting 21st Century Skills in teaching and learning.
The authors believe that statistics and data foundations are essential in an increasingly AI-native world and should be accessible to students and practitioners from varied backgrounds. The authors have a unique combination of complementary skills, a deep understanding of the core set of fundamental concepts and practice of statistics and data science, and extensive experience and insights into foundational education, that makes this a canonical book for the study of statistics and data in relation to AI.
作者簡介(中文翻譯)
譚拉帕尼·達蘇博士（數學統計學博士，羅切斯特大學，1991年）是一位研究科學家和數據科學專家，專注於計算統計、機器學習和數據質量。她在AT&T貝爾實驗室工作了31年，退休時擔任首席發明科學家，現在在新澤西州的費爾利迪金森大學擔任兼任教授，教授數據挖掘、機器學習和人工智慧。達蘇博士在頂尖期刊和研究會議上發表了大量文章，如SIGMOD、KDD和VLDB，並與西奧多·約翰遜博士共同撰寫了該領域首本關於數據質量的技術書籍《探索性數據挖掘與數據清理》，由約翰·威利出版社於2003年出版。作為一名教育者，達蘇博士致力於指導下一代的定量思考者、計算機科學家和數據科學家。
吉塔·穆爾提博士（教育學博士，聖約翰大學，紐約皇后區，2015年）是一位經驗豐富的教育工作者和管理者，她的研究專注於描述和拆解學生中限制自我的信念，這些信念阻礙他們追求STEM教育/職業。最近，穆爾提博士擔任紐約長島赫里克斯學區的K-12數學主任，該學區是一個高效能的公立學校學區。在她的領導下，該學區在通過專注於系統性變革以創造和維持所有學生更大公平性、可及性和成功的倡議中，顯著提高了學生的成就。穆爾提博士熱衷於在教學和學習中推廣21世紀技能。
作者們相信，在日益以人工智慧為主的世界中，統計和數據基礎是必不可少的，應該對來自不同背景的學生和從業者可及。作者們擁有獨特的互補技能組合，對統計和數據科學的核心基本概念及實踐有深刻的理解，並在基礎教育方面擁有豐富的經驗和見解，使本書成為研究統計和數據與人工智慧相關的經典著作。
