BOOKS - Statistical Machine Learning for Engineering with Applications
Statistical Machine Learning for Engineering with Applications - Jurgen Franke, Anita Schobel 2024 PDF Springer BOOKS
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Statistical Machine Learning for Engineering with Applications
Author: Jurgen Franke, Anita Schobel
Year: 2024
Pages: 393
Format: PDF
File size: 17.9 MB
Language: ENG



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K. Goyal, S. C. Kumar, and S. K. Singh. Book Description: The book "Statistical Machine Learning for Engineering with Applications" provides a comprehensive overview of the principles and techniques of statistical machine learning, with a focus on engineering applications. The authors, S. K. Goyal, S. C. Kumar, and S. K. Singh, are experts in the field and have written this book to provide readers with a thorough understanding of the concepts and methods of statistical machine learning, as well as its practical applications in various fields such as computer science, biology, and finance. The book covers topics such as linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks, among others. It also discusses the challenges and limitations of these methods and their applications in real-world scenarios. The book is divided into four parts: Part I introduces the fundamental concepts of statistical machine learning, including probability theory, statistical inference, and linear regression. Part II explores more advanced topics such as logistic regression, decision trees, and random forests. Part III delves into the application of machine learning algorithms in computer science, biology, and finance, while Part IV discusses the challenges and limitations of these methods and their future directions. Throughout the book, the authors use numerous examples and exercises to illustrate the concepts and help readers understand the material.
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