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Название: Machine Learning Methods for Engineering Application Development
Автор: Prasad Lokulwar, Basant Verma, N. Thillaiarasu
Издательство: Bentham Books
Год: 2022
Страниц: 240
Язык: английский
Формат: pdf (true), epub
Размер: 26.1 MB
This book is a quick review of Machine Learning methods for engineering applications. It provides an introduction to the principles of Machine Learning and common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field. Next, it offers some guidelines on applying Machine Learning methods to software engineering tasks. Finally, it gives an outlook into some of the futuredevelopments and possibly new research areas of Machine Learning and Artificial Intelligence in general. Techniques highlighted in the book include: Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural network. Finally, it also intends to be a reference book. This book is meant to be an introduction to Artificial Intelligence (AI), Machine Learning, and its applications in Industry 4.0. It explains the basic mathematical principles but is intended to be understandable for readers who do not have a backgroundin advanced mathematics.
Автор: Prasad Lokulwar, Basant Verma, N. Thillaiarasu
Издательство: Bentham Books
Год: 2022
Страниц: 240
Язык: английский
Формат: pdf (true), epub
Размер: 26.1 MB
This book is a quick review of Machine Learning methods for engineering applications. It provides an introduction to the principles of Machine Learning and common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field. Next, it offers some guidelines on applying Machine Learning methods to software engineering tasks. Finally, it gives an outlook into some of the futuredevelopments and possibly new research areas of Machine Learning and Artificial Intelligence in general. Techniques highlighted in the book include: Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural network. Finally, it also intends to be a reference book. This book is meant to be an introduction to Artificial Intelligence (AI), Machine Learning, and its applications in Industry 4.0. It explains the basic mathematical principles but is intended to be understandable for readers who do not have a backgroundin advanced mathematics.