Why Machines Learn

Why Machines Learn offers an accessible yet rigorous exploration of the mathematical and conceptual foundations underlying artificial intelligence and machine learning. Anil Ananthaswamy takes readers on a journey through the key ideas that enable computers to learn from data, from the basics of statistical learning theory to neural networks and deep learning. Rather than focusing on coding or implementation details, the book illuminates the elegant mathematical principles and ingenious algorithms that make modern AI possible, tracing the field's evolution from its theoretical roots to today's powerful applications. What sets this book apart is Ananthaswamy's gift for making complex mathematical concepts understandable without dumbing them down. He weaves together history, interviews with pioneering researchers, and clear explanations of topics like gradient descent, backpropagation, and probability theory. The narrative reveals not just how machine learning works, but why these particular approaches succeed and what fundamental insights drove their development. It's an ideal read for anyone curious about the intellectual underpinnings of AI—whether you're a student seeking deeper understanding, a professional wanting to grasp the theory behind the tools you use, or simply a curious reader fascinated by one of the most transformative technologies of our time.