Algebra: Solving for x, factoring polynomials, exponents, logarithms, and understanding functions (f(x)). Richard Rusczyk
Geometry & Trigonometry: Shapes, spatial logic, the unit circle, and sine/cosine functions.
Pre-Calculus: Combining algebra and trig to handle limits and prepare for the study of change.
Calculus 1 (Differential): Understanding the derivative (the slope of a curve).
Discrete Mathematics: Logic, set theory, and graph theory. This can be started anytime after Algebra 2 and is the “math of computer logic.”
Linear Algebra: Matrices and vectors. This is the most important math for AI data representation; start this after Calculus 1.
Calculus 2 (Integral): Accumulating quantities (area under a curve) and infinite series.
Calculus 3 (Multivariable): Taking derivatives and integrals with many variables at once. This combines with Linear Algebra for AI optimization.
Probability & Statistics: Using Calculus and Set Theory to model uncertainty and data distributions.
Specialized Master’s Math: Numerical Methods (math for computers), Information Theory (data compression), and Optimization Theory (complex decision making).
Mathematical Proofs: The practice of proving why math rules work, usually integrated into Discrete Math and Linear Algebra courses.