A collection of math textbooks on everything related to Machine Learning.
The list is made based upon a Quora answer by Prof. Sridhar Mahadevan.
- Linear Algebra - Strang
- In All Likelihood by Yudi Pavitan
- Convex optimization by Boyd
- Optimization in vector spaces by Luenberger
- Causal representations in statistics by Judea Pearl
- Group representations in probability and statistics by Persi Diaconis
- Linear statistical models by C. R. Rao
- Convex analysis by Rockafellar
- The Symmetric Group by Sagan
- Applied Math by Strang
- Mulitivariate models of incomplete data by Shafer
- Neurodynamic programming by Tsitsiklis and Bertsekas
- Non-cooperative games by John Nash
- Best approximation in inner product spaces by Deutsch
- Algebra by Lang
- Topological Manifolds by Lee
- Smooth Manifolds by Lee
- Riemannian manifolds by Lee
- Set theory by Paul Halmos
- Measure theory by Paul Halmos
- Probability: independence, exchangeability, martingales by Chow and Teicher
- Computational homology by Kaczynski
- The Topology of Fiber Bundles by Steenrod
Some other books: