Math Books to Complement Murphy's Probabilistic Machine Learning

To fully grasp Kevin Murphy’s Probabilistic Machine Learning series, a strong mathematical foundation is essential. Here’s a concise list of recommended math books, organized by topic:

Core Foundations

Linear Algebra

• Gilbert Strang, Introduction to Linear Algebra (5th/6th Ed)
  Classic introduction to vector spaces, eigenvalues, and SVD.
• Lloyd Trefethen & David Bau, Numerical Linear Algebra
  Focuses on practical computations (SVD, QR, Cholesky).

Probability

• Joseph Blitzstein & Jessica Hwang, Introduction to Probability (2nd Ed)
  Intuitive undergraduate introduction.
• Geoffrey Grimmett & David Stirzaker, Probability and Random Processes (3rd Ed)
  Advanced undergraduate/first-year graduate coverage.
• Achim Klenke, Probability Theory: A Comprehensive Course (2nd Ed)
  Rigorous measure-theoretic treatment (essential for M2).

Statistics

• George Casella & Roger Berger, Statistical Inference (2nd Ed)
  Definitive graduate text on classical and Bayesian inference.
• Larry Wasserman, All of Statistics
  Concise overview of key topics.

Optimization

• Stephen Boyd & Lieven Vandenberghe, Convex Optimization
  Essential for convex problems in ML.
• Jorge Nocedal & Stephen Wright, Numerical Optimization (2nd Ed)
  Advanced algorithms (quasi-Newton, SQP).

Supplemental Reading

• Christopher Bishop, Pattern Recognition and Machine Learning
  Clear explanations of probabilistic ML.
• David MacKay, Information Theory, Inference, and Learning Algorithms
  Unique perspective on Bayesian methods.