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TABLE OF CONTENTS16 sections

Mathematics for Machine Learning

01Mathematics for Machine Learning 02Mathematical Foundations Logic And Notation 03Vectors Matrices And Linear Systems 04Matrix Decompositions And Spectral Methods 05Analytic Geometry Projections And High Dimensional Spaces 06Multivariable Calculus And Automatic Differentiation 07Probability And Random Variables 08Statistical Inference And Estimation 09Statistical Learning Theory And Generalization 10Mathematical Optimization For Machine Learning 11Numerical Methods And Computational Stability 12Information Theory And Probabilistic Losses 13Similarity Kernels And Dimensionality Reduction 14Probabilistic Graphical Latent And Sequential Models 15Decision Theory Causal Reasoning And Business Analytics 16Robust Responsible And Emerging Mathematics For Ai

Chapter 1

Mathematical Foundations Logic And Notation

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