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254227111
Mathematics for AI and Machine Learning: A Comprehensive Mathematical Reference for Artificial Intelligence and Machine , (Hardcover)
Item #:
254227111
XOF 108736
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Product Details
- Mathematics 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.Companion Website: https: //math4ai.orgThe 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.Part 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.Part 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.Part 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.What 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.Designed 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.
| Book format | Hardcover |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | March, 2026 |
| Pages | 600 |
| Subgenre | Mathematical & Statistical Software |
| Series title | No Series |
| Edition | 1 |
| Publisher | Math4ai Press |
| Translated from | English |
| Language | English |
| Edu focus | Mathematics |
| Educational level | General |
| Is collectible | N |
| Binding type | Case Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 7.00 x 1.31 x 10.00 in (17.8 x 3.3 x 25.4 cm) |
| Assembled product weight | 2.75 lb (1.25 kg) |
| Bisac subject heading | Computers |
Product Description
Mathematics for AI and Machine Learning: A Comprehensive Mathematical Reference for Artificial Intelligence and Machine , (Hardcover)
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