Machine Learning and AI with Simple Python and Matlab Scripts: Courseware for Non-computing Majors

Machine Learning and AI with Simple Python and Matlab Scripts: Courseware for Non-computing Majors (IEEE Press)

Machine Learning and AI with Simple Python and Matlab Scripts: Courseware for Non-computing Majors (IEEE Press)

Author: M. Umit Uyar (Author)

Publisher finelybook 出版社:‏ ‎ Wiley-IEEE Press

Edition 版本:‏ ‎ 1st edition

Publication Date 出版日期:‏ ‎ 2025-04-1

Language 语言: ‎ English

Print Length 页数: ‎ 384 pages

ISBN-10: ‎ 1394294956

ISBN-13: ‎ 9781394294954

Book Description

A practical guide to AI applications for Simple Python and Matlab scripts

Machine Learning and AI with Simple Python and Matlab Scripts: Courseware for Non-computing Majors introduces basic concepts and principles of machine learning and artificial intelligence to help readers develop skills applicable to many popular topics in engineering and science. Step-by-step instructions for simple Python and Matlab scripts mimicking real-life applications will enter the readers into the magical world of AI, without requiring them to have advanced math and computational skills. The book is supported by instructor only lecture slides and sample exams with multiple-choice questions.

Machine Learning and AI with Simple Python and Matlab Scripts includes information on:

  • Artificial neural networks applied to real-world problems such as algorithmic trading of financial assets, Alzheimer’s disease prognosis
  • Convolution neural networks for speech recognition and optical character recognition
  • Recurrent neural networks for chatbots and natural language translators
  • Typical AI tasks including flight control for autonomous drones, dietary menu planning, and route planning
  • Advanced AI tasks including particle swarm optimization and differential and grammatical evolution as well as the current state of the art in AI tools

Machine Learning and AI with Simple Python and Matlab Scripts is an accessible, thorough, and practical learning resource for undergraduate and graduate students in engineering and science programs along with professionals in related industries seeking to expand their skill sets.

From the Back Cover

A practical guide to AI applications for Simple Python and Matlab scripts

Machine Learning and AI with Simple Python and Matlab Scripts: Courseware for Non-computing Majors introduces basic concepts and principles of machine learning and artificial intelligence to help readers develop skills applicable to many popular topics in engineering and science. Step-by-step instructions for simple Python and Matlab scripts mimicking real-life applications will enter the readers into the magical world of AI, without requiring them to have advanced math and computational skills. The book is supported by instructor only lecture slides and sample exams with multiple-choice questions.

Machine Learning and AI with Simple Python and Matlab Scripts includes information on:

  • Artificial neural networks applied to real-world problems such as algorithmic trading of financial assets, Alzheimer’s disease prognosis
  • Convolution neural networks for speech recognition and optical character recognition
  • Recurrent neural networks for chatbots and natural language translators
  • Typical AI tasks including flight control for autonomous drones, dietary menu planning, and route planning
  • Advanced AI tasks including particle swarm optimization and differential and grammatical evolution as well as the current state of the art in AI tools

Machine Learning and AI with Simple Python and Matlab Scripts is an accessible, thorough, and practical learning resource for undergraduate and graduate students in engineering and science programs along with professionals in related industries seeking to expand their skill sets.

About the Author

M. Ümit Uyar is a Professor at the City College of the City University of New York, USA. Dr. Uyar is an IEEE Fellow, author, co-author and co-editor of seven books, holder of seven U.S. patents, and developer of AI and game theory-based algorithms for applications in topology control in mobile networks and personalized cancer treatment.

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