Mathematics for Machine Learning and AI

Mathematics for Machine Learning and AI provides a foundational and practical understanding of the core mathematical concepts that underpin modern artificial intelligence systems. It covers essential topics such as linear algebra, calculus, probability theory, statistics, optimization, and discrete mathematics, all tailored to their applications in machine learning and AI. This book bridges the gap between mathematical theory and practical implementation, making complex topics accessible through clear explanations, real-world examples, and hands-on problem-solving. Readers will learn how eigenvalues, gradients, probability distributions, and optimization algorithms drive intelligent systems-from neural networks and decision trees to deep learning and reinforcement learning. Designed for students, educators, and professionals, the book balances theoretical rigor with intuitive insights, offering both the mathematical depth and applied knowledge needed to excel in the evolving fields of data science, AI, and machine learning.

Autorentext

Dr. Randhir Singh Baghel is an Associate Professor at Poornima University, Jaipur, Rajasthan, India, with 17+ years of academic experience. He has published 32 research papers in reputed journals and authored two books: AI and Complex Systems: Forecasting for Smarter Societal Decisions and Mathematics for Data Science and Artificial Intelligence.