Mathematics for Machine Learning
Marc Peter Deisenroth, A. Aldo Faber, Cheng Soon Ong
Cambridge University Press textbook covering linear algebra, calculus, probability, and optimization with direct connections to ML algorithms. CC licensed.
Loading the book...
Keep reading
-
Next in this topic
Mathematical Analysis of Machine Learning AlgorithmsAdvanced mathematical treatment of ML theory covering optimization, generalization bounds, kernel methods, and deep learning convergence.
Verified PDF · Secure download - Mathematical Background for Machine Learning
Concise Berkeley CS 189 review covering linear algebra, probability, and calculus essentials needed before studying ML.
Verified PDF · Secure download - Foundations of Machine Learning
Rigorous treatment of ML foundations covering PAC learning, Rademacher complexity, boosting, and kernel methods. Ideal for readers with strong mathematical background.
Verified PDF · Secure download - Machine Learning - Supervised Techniques
Comprehensive lecture notes on supervised ML techniques from the creator of LSTM networks, covering regression, classification, and ensemble methods.
Verified PDF · Secure download - Interpretable Machine Learning
Practical guide to making ML models explainable, covering SHAP, LIME, partial dependence plots, and feature importance methods.
Verified PDF · Secure download