Mathematics is the foundation of machine learning algorithms. To understand the shortcomings of existing algorithms and develop more effective methods, it is essential to understand the mathematical concepts underlying these algorithms and their operational principles. This book serves as an introductory resource, outlining the preliminary concepts and offering insights into the mathematical foundations and operational mechanisms of machine learning algorithms. It describes the basic equations and interrelates the questions arising during practical applications of machine learning with the basic mathematical picture of the algorithms used.

Features

• Introduces machine learning, highlights the central role of algorithms in machine learning, and explains the core mathematical prerequisites to understanding machine learning algorithms

• Systematically examines the sequential steps of classical machine learning algorithms used for classification of data sets into distinct groups; regression, clustering analysis,

• Provides an overview of value, policy, and model-based reinforcement learning algorithms.

This book is for academicians, scholars, students, and professionals engaged in the study of machine learning and artificial intelligence.

About the Author

Vinod Kumar Khanna, Ph.D. (Physics) is an emeritus scientist, Council of Scientific and Industrial Research (CSIR), India, and Emeritus Professor, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India; a retired chief scientist, CSIR-Central Electronics Engineering Research Institute, Pilani, India and professor, AcSIR, India. He has worked for more than 37 years on the design, fabrication, and characterization of power semiconductor devices, MEMS, and nanotechnology-based sensors. He has published 194 research papers in refereed journals and conference proceedings, 23 books, and six chapters in edited books. He has five patents to his credit, including two US and three Indian patents.

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