This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites.

This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory.

We focus on applied math concepts tailored specifically for machine learning — linear algebra, calculus, probability, and optimization — all explained in context with real ML models and intuitive visuals. This …

Learn about the prerequisite mathematics for applications in data science and machine learning.

Apr 23, 2020 · The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

This textbook is meant to summarize the mathematical underpinnings of important machine learning applications and to connect the mathematical topics to their use in machine learning problems.

We start with an intensive review of concepts from linear algebra, analytic geometry, vector calculus, optimization, and probability, and then apply them in detail to machine learning methods such as …