Mathematics for Machine Learning 1st Edition

This self-contained textbook provides the essential mathematical foundations needed to grasp core machine learning techniques, covering topics such as linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. These subjects are often taught separately, making it challenging for students or professionals in data science and computer science to gain a cohesive understanding.

Designed to bridge the gap between traditional math courses and machine learning applications, the book introduces each mathematical concept with minimal prerequisites and applies them directly to four fundamental machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines.

Whether you're new to the math or looking to deepen your understanding of its applications in machine learning, this textbook offers both a strong conceptual foundation and practical insight. Each chapter features detailed examples, exercises to reinforce learning, and access to online programming tutorials to support hands-on experience.