Mathematics for Machine Learning

Unlock the world of machine learning with Mathematics for Machine Learning by Marc Peter Deisenroth. Published by Cambridge University Press in 2020, this comprehensive hardback textbook spans 398 pages and is designed to equip readers with the essential mathematical foundations required for mastering machine learning techniques.

This self-contained resource covers a wide array of crucial topics, including linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, and probability and statistics. With minimal prerequisites, it is ideal for students and professionals eager to delve into the intricacies of machine learning.

Whether you're a beginner or looking to enhance your existing knowledge, Mathematics for Machine Learning serves as an invaluable guide to navigating the mathematical landscape of this exciting field.