I have a few math courses to pick from in my studies, and I would love some insights on which math courses from the following list are the most relevant for machine learning. I will note - to avoid this question being closed on "opinion based" cause, that my question does not involve which math courses I like or interests me, but being objectively as possible - which math courses are the most relevant for machine learning.
This are the following courses I can choose from:
- Numerical Analysis
- Ordinary Differential Equations 1
- Theory of Functions of a Complex Variable 1
- Linear Algebra 3
- Number Theory
- Probability for Sciences
Since I know Linear Algebra and Probability are very important for Machine Learning, I will note that I have already studied Linear Algebra 1 and 2 (which include matrices, linear transformations, linear and bilinear operators, normed vector spaces, eigenvalues and eigenvectors, diagonalization, Jordan form, hermitian spaces, unitary and orthogonal transformations, and some more subjects), while Linear Algebra 3 syllabus' is mainly about groups, isomorphism theorems, Lagrange's theorem, group actions, Sylow's theorems, finitely generated Abelian groups, solvable groups, the symmetric group and free groups.
Also, I already had Probability 101 which is mainly discrete probability and includes basic probability theory, discrete distributions (such as Poisson, binomial, HG and more), random variables, markov's and Chebyshev's inequalities, expected value, variance and covariance, CLT and Markov chains, while the "Probability for Sciences" course has in its syllabus continues distributions, PDF, MGF, convergence of random variables and convergence in probability, the multi-dimensional normal distribution and some more subjects.
I will also note I have studied the following math courses as well: Calculus 1 and 2 and Discrete Math.