Machine Learning and Artificial Intelligence
Brand: Springer
Categories: Books, Computers & Technology, Computer Science



Dimensions: 6.14 x 0.86 x 9.21 inches
Language: English
ISBN-10: 3030266214
Publisher: Springer
Print length: 284 pages
Publication date: October 7, 2019
ISBN-13: 978-3030266219
Edition: 1st ed. 2020
subtitle: 1st ed. 2020 Edition
author: Joshi
review: Editorial Reviews Review “With a good balance of theory and practice, the book effectively combines machine learning (ML) and artificial intelligence (AI) topics. Unlike other books on AI, Machine Learning and Artificial Intelligence is not very mathematically intensive, which makes it easier to read. Overall, its language is very easy to follow. Each chapter has introduction and conclusion sections, and many helpful figures explain the concepts.” (Computing Reviews) “This book provides a thorough description of mathematical tools needed to learn and practice Machine Learning for many real time applications ...” (Sitharama Iyengar, University Distinguished Professor, Florida International University, Miami, Florida) From the Back Cover This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The forth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. Presents a full reference to artificial intelligence and machine learning techniques - in theory and application; Provides a guide to AI and ML with minimal use of mathematics to make the topics more intuitive and accessible; Connects all ML and AI techniques to applications and introduces implementations. About the Author Dr. Ameet Joshi received his PhD from Michigan State University in 2006. He has over 15 years of experience in developing machine learning algorithms in various different industrial settings including Pipeline Inspection, Home Energy Disaggregation, Microsoft Cortana Intelligence and Business Intelligence in CRM. He is currently a Data Science Manager at Microsoft. Previously, he has worked as Machine Learning Specialist at Belkin International and a Director of Research at Microline Technology Corp. He is a member of several technical committees, has published in numerous conference and journal publications and contributed to edited books. He also has two patents and have received several industry awards including and Senior Membership of IEEE (which only 8% of members achieve). Read more