Concept map

How it all fits together — follow the arrows from one idea to what it unlocks. Linear algebra flows left into probability; hover a concept to trace its prerequisites, and click to jump to the lesson. Node colour shows your mastery.

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Vectors & maps

Systems & determinants

Eigen-structure

Inner products

Spectral & SVD

Probability

Random variables

Expectation

Vectors Spaces & basis Linear maps Matrices Linear systems Determinants Eigenvalues Diagonalization Inner products Projections Least squares Spectral theorem SVD PCA Probability spaces Conditional prob.Bayes’ theorem Independence Random variables Discrete distributions Expectation Variance

Joint distributions & covariance (Module 10) will formally close the loop between PCA and probability — the dashed link is a preview.