Machine Learning Mathematics - Foundations and Applications

“Machine Learning Mathematics: Foundations and Applications" introduce the fundamental mathematical concepts that underpin machine learning algorithms and their practical implementation. The text systematically covers essential areas like linear algebra, probability and statistics, and calculus, alongside crucial optimization techniques. It further explores the mathematical basis of core machine learning models such as linear regression, SVMs, and neural networks, and touches upon advanced topics including graphical models and Bayesian methods. Ultimately, the book aims to equip readers with a strong theoretical understanding necessary for both comprehending and effectively applying machine learning in various domains. Read more