Mathematics for Machine Learning by Cambridge University Press is the ultimate self-contained textbook bridging the gap between high school mathematics and the mathematical knowledge required to truly understand machine learning.
- Covers all essential ML math in one place — Linear Algebra, Analytic Geometry, Matrix Decompositions, Vector Calculus, Optimization, Probability & Statistics
- Derives 4 central ML methods from scratch — Linear Regression, PCA, Gaussian Mixture Models & Support Vector Machines
- Written by DeepMind Chair Marc Peter Deisenroth (UCL), A. Aldo Faisal (Imperial College London) & Cheng Soon Ong (CSIRO/ANU)
- Praised by Joelle Pineau (McGill & Facebook), Pieter Abbeel (UC Berkeley) & SIAM Review as outstanding
- Perfect for data science students, ML engineers, AI researchers & anyone building solid mathematical foundations for machine learning
