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Math for AI

Linear algebra, probability and calculus as actually used.

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This course has 3 lessons. Start with the first one and use the next / previous links at the bottom of each lesson — the sidebar keeps the whole course in order.

Lessons

  1. Vectors and matrices for MLThe two objects that every model is built from: vectors as features, matrices as batched linear maps, and the shape rules that decide whether your code runs.
  2. Probability and distributionsRandom variables, the distributions ML actually uses, and how cross-entropy is just negative log likelihood in disguise.
  3. Gradients and calculus intuitionDerivatives as sensitivity, the chain rule behind backpropagation, and how to tell a learning-rate problem from an architecture problem.

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Last refreshed 2026-09-17.