{"product_id":"mathematics-for-ai-and-machine-learning-a-comprehensive-mathematical-reference-for-artificial-intelligence-and-machine-learning-hardcover","title":"Mathematics for AI and Machine Learning: A Comprehensive Mathematical Reference for Artificial Intelligence and Machine Learning - Hardcover","description":"\u003cp\u003eby \u003cb\u003eFuheng Wu\u003c\/b\u003e (Author), \u003cb\u003eYu Guo\u003c\/b\u003e (Foreword by)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eMathematics for AI and Machine Learning is a comprehensive, graduate-level textbook that provides the rigorous mathematical foundations essential for understanding modern artificial intelligence and machine learning systems.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eCompanion Website: https: \/\/math4ai.org\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe book spans 21 chapters organized into four parts. Part I (Chapters 1-10) covers linear algebra fundamentals: vector spaces, inner products, matrix operations, subspaces, orthogonality, QR decomposition, LU factorization, eigendecomposition, symmetric matrices, and the Singular Value Decomposition (SVD)-establishing the mathematical foundation for representation in AI.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003ePart II (Chapters 11-12) addresses differentiation and optimization: matrix calculus with gradients and Hessians, and optimization methods including gradient descent and its variants-formalizing learning as structured search in parameter space.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003ePart III (Chapters 13-16) introduces probability and information theory: probability and random variables, entropy and KL divergence, the Evidence Lower Bound (ELBO), variational inference and latent variable models, and Bellman equations for reinforcement learning-shifting the perspective from fitting functions to modeling distributions.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003ePart IV (Chapters 17-21) ventures into score functions, dynamics, and diffusion: score functions and energy-based models, Langevin dynamics and sampling methods, stochastic differential equations with Itô calculus, ODE\/SDE continuous limits of algorithms, and Fokker-Planck equations governing distribution dynamics-framing generative modeling as the study of distributional dynamics.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eWhat distinguishes this textbook is its seamless integration of mathematical rigor with practical AI\/ML applications. Each concept is motivated by real-world problems in machine learning, deep learning, large language models, graph neural networks, reinforcement learning, and modern generative frameworks. The full-color figures illuminate complex ideas, while extensive exercises reinforce understanding.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eDesigned for graduate students, researchers, and experienced practitioners, this book serves as both a learning resource and a comprehensive reference. Whether you're building foundation models, researching novel architectures, or seeking deeper understanding of the mathematics powering AI systems, this textbook provides the essential theoretical toolkit.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 600\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.31 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e March 01, 2026\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":57224238563633,"sku":"9798995152361","price":172.78,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1004\/9532\/7537\/files\/8atoOZ1m7k9798995152361.webp?v=1793411496","url":"https:\/\/mitchellofficial.com\/products\/mathematics-for-ai-and-machine-learning-a-comprehensive-mathematical-reference-for-artificial-intelligence-and-machine-learning-hardcover","provider":"MITCHELL","version":"1.0","type":"link"}