There is no required textbook, but the following fine texts are recommended.
Tong Zhang. Mathematical Analysis of Machine Learning Algorithms. Cambridge University Press, 2023.
Moritz Hardt and Benjamin Recht. Patterns, Predictions, and Actions. Princeton University Press, 2022.
Kevin Patrick Murphy. Probabilistic Machine Learning: Advanced Topics. MIT Press, 2023.
Kevin Patrick Murphy. Probabilistic Machine Learning: An Introduction. MIT Press, 2022.
Aston Zhang, Zack C. Lipton, Mu Li and Alex J. Smola. Dive into Deep Learning. Cambridge University Press, 2023.
Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
For those who need to refresh math (all of us?):
Thomas Mack. The Math You Need: A Comprehensive Survey of Undergraduate Mathematics. MIT Press, 2023.
Thomas A. Garrity. All the Math You Missed (But Need to Know for Graduate School). 2nd Edition. Cambridge University Press, 2021.
Gilbert Strang. Linear Algebra and Learning from Data. SIAM, 2019.
Stephen Boyd and Lieven Vandenberghe. Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares. Cambridge University Press, 2018.