The Mathematics of Machine Learning

Machine learning is built on mathematics-but many explanations stop just when the mathematics becomes important.

The Mathematics of Machine Learning provides a rigorous yet readable foundation for understanding the mathematical ideas behind modern machine learning. Designed for advanced undergraduates, career-switchers, programmers, and technical readers with some calculus, this book connects mathematical theory directly to the way machine-learning systems actually learn.

You will explore the mathematics of:

• Linear algebra, vectors, matrices, and transformations
• Multivariable calculus, derivatives, gradients, and the chain rule
• Probability distributions, conditional probability, and expectation
• Statistics, estimation, inference, and model evaluation
• Loss functions and mathematical optimization
• Gradient descent and iterative learning
• Regularization and generalization
• Neural networks and backpropagation
• Learning theory and the principles behind model performance

Rather than presenting mathematics as disconnected formulas, the book explains what each mathematical concept means, why it matters, and how it becomes part of a machine-learning system.

Short Python and NumPy examples reinforce selected concepts without turning the book into a programming tutorial. Equations, derivations, visual explanations, and practical interpretations work together to build genuine mathematical intuition.

Whether you are preparing for advanced study, transitioning into machine learning, or simply want to understand what happens beneath the algorithms, this book provides the mathematical foundation needed to move confidently into deep learning, Transformers, and generative AI.

This is Book 1 of The Mathematics of Modern AI.