Mathematics

Probability, statistics, linear algebra, calculus, and the mathematical foundations behind AI.

Probability & Statistics

Probability theory, estimation methods, and statistical inference.

Vectors, matrices, eigenvalues, and the geometry behind machine learning.

Calculus & Optimization

Derivatives, gradients, and optimization methods used to train models.

Information Theory

Entropy, KL divergence, mutual information, and their role in learning.

Numerical Methods

Numerical linear algebra, interpolation, and computational techniques.

Discrete Mathematics

Combinatorics, graph theory, logic, and the math behind algorithms.

Differential Equations

ODEs, PDEs, dynamical systems, and continuous-time modeling for AI.