A comprehensive guide explaining the mathematical foundations of AI, covering linear algebra, calculus, probability, statistics, and optimization theory. The book breaks down complex mathematical concepts into accessible explanations with practical Python examples, showing how matrices, vectors, determinants, eigenvalues, and other mathematical tools form the backbone of modern AI systems like large language models. It emphasizes an engineering perspective over pure theory, connecting abstract math concepts to real-world applications in machine learning and control systems.
Table of contents
Chapter 1: Background on this BookChapter 2: The Architecture of MathematicsChapter 3: The Field of Artificial IntelligenceChapter 4: Linear Algebra - The Geometry of DataChapter 5: Multivariable Calculus - Change in Many DirectionsChapter 6: Probability & Statistics - Learning from UncertaintyChapter 7: Optimization Theory - Teaching Machines to ImproveConclusion: Where Mathematics and AI MeetAbout the AuthorSort: