What Every Engineer Should Know about Data-Driven Analytics
Srinivasan, Satish Mahadevan, Laplante, Phillip A.
- 出版商: CRC
- 出版日期: 2023-04-13
- 售價: $5,750
- 貴賓價: 9.5 折 $5,463
- 語言: 英文
- 頁數: 260
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1032235438
- ISBN-13: 9781032235431
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商品描述
What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the theoretical concepts and approaches of machine learning that are used in predictive data analytics. By introducing the theory and by providing practical applications, this text can be understood by every engineering discipline. It offers a detailed and focused treatment of the important machine learning approaches and concepts that can be exploited to build models to enable decision making in different domains.
- Utilizes practical examples from different disciplines and sectors within engineering and other related technical areas to demonstrate how to go from data, to insight, and to decision making.
- Introduces various approaches to build models that exploits different algorithms.
- Discusses predictive models that can be built through machine learning and used to mine patterns from large datasets.
- Explores the augmentation of technical and mathematical materials with explanatory worked examples.
- Includes a glossary, self-assessments, and worked-out practice exercises.
Written to be accessible to non-experts in the subject, this comprehensive introductory text is suitable for students, professionals, and researchers in engineering and data science.
商品描述(中文翻譯)
《每位工程師都應該了解的數據驅動分析》提供了對機器學習的理論概念和方法的全面介紹，這些方法在預測性數據分析中得到應用。通過介紹理論並提供實際應用，本書可被各種工程學科理解。它詳細且專注地介紹了重要的機器學習方法和概念，這些方法和概念可以用於建立模型，以實現不同領域的決策。
- 利用不同工程學科和其他相關技術領域的實際例子，展示如何從數據到洞察力，再到決策。
- 介紹了建立利用不同算法的模型的各種方法。
- 討論了通過機器學習建立的預測模型，並用於從大型數據集中挖掘模式。
- 探討了將技術和數學材料與解釋性的實例相結合的方法。
- 包括詞彙表、自我評估和解答練習。
本書旨在讓非專家讀者能夠理解，適合工程和數據科學的學生、專業人士和研究人員閱讀。
作者簡介
Satish M. Srinivasan received his B.E. in Information Technology from Bharathidasan University, India and M.S. in Industrial Engineering and Management from the Indian Institute of Technology Kharagpur, India. He earned his Ph.D. in Information Technology from the University of Nebraska at Omaha. Prior to joining Penn State Great Valley, he worked as a postdoctoral research associate at University of Nebraska Medical Center, Omaha. Dr. Srinivasan teaches courses related to database design, data mining, data collection and cleaning, computer, network and web securities, and business process management. His research interests include data aggregation in partially connected networks, fault-tolerance, software engineering, social network analysis, data mining, machine learning, Big Data, and predictive analytics and bioinformatics.
Phil Laplante is Professor of Software and Systems Engineering at The Pennsylvania State University. He received his B.S., M.Eng., and Ph.D. from Stevens Institute of Technology and an MBA from the University of Colorado. He is a Fellow of the IEEE and SPIE and has won international awards for his teaching, research, and service. From 2010 to 2017 he led the effort to develop a national licensing exam for software engineers.
He has worked in avionics, CAD, and software testing systems and he has published 40 books and more than 300 scholarly papers. He is a licensed professional engineer in the Commonwealth of Pennsylvania. He is also a frequent technology advisor to senior executives, investors, entrepreneurs, and attorneys and actively serves on corporate technology advisory boards.
His research interests include artificial intelligent systems, critical systems, requirements engineering, and software quality and management. Prior to his appointment at Penn State he was a software development professional, technology executive, college president, and entrepreneur.
作者簡介(中文翻譯)
Satish M. Srinivasan在印度的Bharathidasan大學獲得了資訊技術學士學位，並在印度的印度理工學院Kharagpur獲得了工業工程與管理碩士學位。他在內布拉斯加大學奧馬哈分校獲得了資訊技術博士學位。在加入賓夕法尼亞州大學大谷分校之前，他曾在內布拉斯加醫學中心奧馬哈分校擔任博士後研究員。Srinivasan博士教授與資料庫設計、資料採礦、資料收集和清理、電腦、網絡和網頁安全以及業務流程管理相關的課程。他的研究興趣包括部分連接網絡中的數據聚合、容錯能力、軟件工程、社交網絡分析、數據採礦、機器學習、大數據和預測分析以及生物信息學。
Phil Laplante是賓夕法尼亞州立大學的軟件和系統工程教授。他在史蒂文斯理工學院獲得了學士、碩士和博士學位，並在科羅拉多大學獲得了工商管理碩士學位。他是IEEE和SPIE的會士，並因其教學、研究和服務而獲得國際獎項。從2010年到2017年，他領導了開發國家軟件工程師專業資格考試的工作。
他曾在航空電子、CAD和軟件測試系統方面工作，並出版了40本書和300多篇學術論文。他是賓夕法尼亞州的註冊專業工程師。他還經常擔任高級執行官、投資者、企業家和律師的技術顧問，並積極參與企業技術顧問委員會的工作。
他的研究興趣包括人工智能系統、關鍵系統、需求工程和軟件質量和管理。在加入賓夕法尼亞州大學之前，他曾擔任軟件開發專業人員、技術執行官、學院校長和企業家的職位。
