Well-written textbooks (or even theses) are the fastest way to learn *technical* topics that have achieved **critical mass**. Inspired by a similarly titled post, I have my own evolving list.

While I have not read most books cover to cover, I have consistently referred back to more than a few chapters of relevance. I am convinced that the rest of the book is worth reading. Often, multiple books cater to overlapping topics, and provide complementary strengths to aid understanding. When multiple books are specified within each (sub-)section, it is safe to assume that as a "soft" recommendation order.

- Mathematical Methods for Physics and Engineering by K. F. Riley, M. P. Hobson, S. J. Bence (2006)

Numerical Optimization by Jorge Nocedal, Stephen J. Wright (2006)

Convex Optimization by Stephen Boyd and Lieven Vandenberghe (2004)

- Applied Stochastic Differential Equations by Simo Särkkä and Arno Solin (2019)

So You Want to Study Mathematics… has a reasonable starter list for some core topics in mathematics.

Linear Algebra Done Right by Sheldon Axler (1995; 2015)

Introduction to Linear Algebra by Gilbert Strang (1993; 2016)

Numerical Linear Algebra by Lloyd N. Trefethen and David Bau, III (1997)

Numerical Algorithms by Justin Solomon (2015)

Fundamentals of Matrix Computations by David S. Watkins (2010)

Matrix Computations by G.H. Golub and C.F. Van Loan (2013)

Pattern Recognition and Machine Learning by Christopher Bishop (2006)

Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville (2016)

Patterns, Predictions, and Actions: A Story about Machine Learning by Moritz Hardt and Benjamin Recht (2021)

Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto (1998; 2018)

Information Theory, Inference and Learning Algorithms by David J. C. MacKay (2003)

Probabilistic Machine Learning by Kevin Murphy (book series 2012, 2021, 2022)

Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams (2006)

Bayesian Reasoning and Machine Learning by David Barber (2012)

Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (2012; 2018)

Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David (2014)