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Mathematics for Machine Learning

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Page
https://mml-book.com

Table of Contents

• Part I: Mathematical Foundations
  • Introduction and Motivation
  • Linear Algebra
  • Analytic Geometry
  • Matrix Decompositions
  • Vector Calculus
  • Probability and Distribution
  • Continuous Optimization
• Part II: Central Machine Learning Problems
  • When Models Meet Data
  • Linear Regression
  • Dimensionality Reduction with Principal Component Analysis
  • Density Estimation with Gaussian Mixture Models
  • Classification with Support Vector Machines

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