Full Description
Machine learning has become a cornerstone of modern data-driven science and technology. For mathematics students and researchers, understanding the mathematical foundations behind machine learning is essential, even if they never work directly with real-world datasets.
This book provides a rigorous yet accessible introduction to the core mathematical ideas that underpin machine learning. Topics such as linear and nonlinear regression, regularization techniques, and the fundamentals of neural networks are explained in detail from a clear mathematical perspective.
Unlike many existing texts that emphasize coding and practical implementation, this book focuses on theoretical results and conceptual understanding. It is designed for readers who want to grasp the mathematics behind machine learning without writing code.
Who should read this book?
Mathematics students and researchers interested in machine learning but with little programming experience.
Scientists and engineers who have applied machine learning in practice and now seek a deeper understanding of its mathematical foundations.
Contents
1 Matrix.- 2 Linear Regression.- 3 Regularization.- 4 Nonlinear Regression.- 5 Shallow Neural Network.- 6 Deep Neural Network.- 7 Batch Normalization.- 8 Support Vector Machine.- 9 Gradient Methods.- 10 Dimensionality Reduction.- A Related Topics.- B Hints to Selected Exercise Problems.- Index.
