Mathematics for Machine Learning

“Mathematics for Machine Learning” is a textbook about, wait for it, mathematics for understanding machine learning. It is split into two parts:

• Part I, which deals with the mathematical foundations for machine learning, namely:
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  • MML - Analytic Geometry
  • MML - Matrix Decompositions
  • MML - Vector Calculus
  • MML - Probability and Distributions
  • MML - Continuous Optimisation
• Part II, which deals with the “four pillars of machine learning”, described as:
  • MML - Linear Regression
  • MML - Dimensionality Reduction
  • MML - Density Estimation
  • MML - Classification with Support Vector Machines