Machine Learning and Artificial Intelligence

2. Auflage 2023
ISBN: 978-3-031-12281-1
Verlag: Springer International Publishing

Buch, Englisch, 271 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 612 g

The new edition of this popular professional book on artificial intelligence (ML) and machine learning (ML) has been revised for classroom or training use. The new edition provides comprehensive coverage of combined AI and 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 fourth 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. Each chapter is accompanied with a set of exercises that will help the reader / student to apply the learnings from the chapter to a real-life problem. Completion of these exercises will help the reader / student to solidify the concepts learned.

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. The book covers a large gamut of topics in the area of AI and ML and a professor can tailor a course on AI / ML based on the book by selecting and re-organizing the sequence of chapters to suit the needs.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

Joshi, Ameet V

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Introduction.- Introduction to AI and ML.- Essential Concepts in Artificial Intelligence and Machine Learning.- Data Understanding, Representation, and Visualization.- Linear Methods.- Perceptron and Neural Networks.- Decision Trees.- Support Vector Machines.- Probabilistic Models.- Dynamic Programming and Reinforcement Learning.- Evolutionary Algorithms.- Time Series Models.- Deep Learning.- Emerging Trends in Machine Learning.- Unsupervised Learning.- Featurization.- Designing and Tuning.- Model Pipelines.- Performance Measurement.- Classification.- Regression.- Ranking.- Recommendations Systems.- Azure Machine Learning.- Open Source Machine Learning Libraries.- Amazon’s Machine Learning Toolkit: Sagemaker.- Conclusion.

Über Autor(innen)

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 Product 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.