Mathematics for Machine Learning By Marc Peter Deisenroth ISBN- 978-1108455145

Analytical geometry, matrix decompositions, vector calculus, optimization, probability, statistics, and linear algebra are the basic mathematical skills required to comprehend machine learning. Because these subjects are typically taught in separate courses, it can be challenging for professionals or students studying computer science or data science to effectively grasp the mathematics. This self-contained textbook introduces mathematical ideas with the fewest requirements, bridging the gap between machine learning and mathematics texts. The four main machine learning techniques—linear regression, principal component analysis, Gaussian mixture models, and support vector machines—are derived from these ideas. These derivations offer a foundation for machine learning texts for students and those with a mathematical background.