Mathematics for Machine Learning

The "Mathematics for Machine Learning" book is a comprehensive resource for those looking to refresh their knowledge of linear algebra and calculus in the context of machine learning. It effectively lays out the necessary mathematical concepts and provides a good introduction to important algorithms in machine learning. However, the book falls short in certain areas. It lacks proofs and explanations for many properties and extensions, making it difficult for readers to fully grasp the concepts. Additionally, it does not cover fundamental theories in machine learning such as PAC learning, VC dimensions, and the No Free Lunch theorem. While the book is a good refresher for those already familiar with the mathematics in machine learning and offers insights into the mathematical intuitions behind popular ML algorithms, it is not beginner-friendly for those without a solid foundation in linear algebra. Overall, "Mathematics for Machine Learning" is a valuable resource for those seeking to understand the mathematical background of machine learning concepts, but it may require additional reading of dedicated mathematics textbooks to form a sound mathematical foundation.