If you’re getting into programming for ML or LLMs, start with discrete mathematics as it builds your foundation in logic, sets, graphs, and combinatorics, all of which come up constantly when you’re dealing with data structures, algorithms, and model architectures.

Then I’d suggest to move on to linear algebra (vectors, matrices, dot products, transformations) since that’s literally how data and weights are represented in neural networks.

You’ll also want calculus, mainly to understand optimization - things like gradients, partial derivatives, and the chain rule are what make models “learn” through gradient descent.

Add in some probability and statistics (Bayes’ theorem is a big one) because it’s the basis of how models reason about uncertainty and update beliefs. Concepts like expected value, variance, and probability distributions (normal, Bernoulli, etc.) will show up everywhere.

There’s more to come when exploring ML and LLMs, but I think these would be a good starting path. Hope this helps and good luck!