Mathematics of Artificial Intelligence

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Comprehensive mathematical foundation for machine learning and AI. Covers key topics in statistics, linear algebra, and optimization as applied to modern AI systems. Topics include quantifying bias and variance, estimation theory (e.g., maximum likelihood estimation, ensemble methods), statistics on graphs, including the configuration model, probability distributions on graphs, summary statistics, and random walks. Optimization topics include gradient descent, continuous and convex optimization, backpropagation, kernel methods, and practical aspects of optimization.