Math

Notes across probability, calculus, linear algebra, and optimization, organized as one mathematical foundation rather than separate silos.

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Probability MIT 6.041 notes on sample spaces, axioms, discrete and continuous models, and the basic laws that make probability a mathematical system.

Calculus Gilbert Strang’s calculus notes on derivatives, integrals, differential equations, Taylor series, and the structural ideas behind first-year calculus.

MIT 18.06SC Linear Algebra Core linear algebra through subspaces, orthogonality, eigenvalues, determinants, Fourier, and geometric intuition, plus synthesis notes.

MIT 18.065: Linear Algebra Applications Linear algebra in data analysis, optimization, neural nets, probability, convolution, Fourier, and matrix methods for machine learning.

Stanford EE 364A: Convex Optimization Convex sets, convex functions, duality, conic viewpoints, and optimization theory with a systems-and-geometry perspective.