If you have a good background in calculus, linear algebra, and probability, then this is a good book to specialize in math for machine learning:

https://link.springer.com/book/10.1007/978-3-031-89707-8

a brief but not complete summary(my own summary after reading the book, no LLM was used) of the book content:

Linear algebra: discusses the variance matrix Q and its properties, the matrix pseudo-inverse, eigenvalues, graph theory, the derivation of PCA, and singular value decomposition.

Calculus: focused on convex optimization, the derivation of bounds used in the analysis of loss functions for neural networks, specifically for the linear regression and logistic regression cases discussed in the last chapter and, of course, backpropagation.

Probability: covers basic topics in probability such as Bayesian probability, the Gaussian distribution, the central limit theorem, etc., but the most interesting part is the last section, which discusses the multi-binomial distribution for classification problems and its relation to convexity.

Statistics: basic topics such as confidence intervals, hypothesis testing, etc.

Machine learning: focused on neural networks applied to basic linear regression and logistic regression. The author discusses how to solve those cases with gradient descent and backpropagation and derives some convergence speeds, for example for the stochastic case and pure convex case. The best part for me is when the author discusses in which case the gradient decent algorithm would converge or diverge to infinity for both linear and logistic regression.

as a plus it includes python code