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The orthogonal projection of b onto a subspace W is proj_W(b), the point in W closest to b. For orthonormal basis {u₁, ..., uₖ}, proj_W(b) = Σ⟨b,uᵢ⟩uᵢ. For subspace spanned by columns of A, proj_W(b) = A(AᵀA)⁻¹Aᵀb. Least squares minimizes ||Ax − b||²; the optimal solution x* satisfies the normal equations AᵀAx* = Aᵀb, found via projection.
From Gram-Schmidt, you know how to convert a basis into an orthonormal basis — a set of mutually perpendicular unit vectors. Orthogonal projections are what makes those orthonormal bases so powerful. The idea is geometric: given a vector b and a subspace W, the orthogonal projection proj_W(b) is the unique point in W that is closest to b. "Closest" means the error vector b − proj_W(b) is perpendicular to every vector in W.
When W has an orthonormal basis {u₁, ..., u_k}, the projection formula is remarkably clean: proj_W(b) = Σ⟨b, uᵢ⟩uᵢ. Each term ⟨b, uᵢ⟩uᵢ is the shadow of b onto one basis direction, and the full projection just sums these shadows. This works because orthonormality decouples the directions — there is no "cross-talk" between basis vectors, so you can handle each coordinate independently. This is exactly what Gram-Schmidt was buying you all along.
Least squares is what happens when you want to solve Ax = b but no exact solution exists — the right-hand side b lies outside the column space of A. Since you cannot hit b exactly, the best you can do is find the x that makes Ax as close to b as possible. The closest point in the column space of A to b is exactly the orthogonal projection of b onto that column space. The minimizer x* satisfies the normal equations AᵀAx* = Aᵀb, which you obtain by projecting b onto col(A). When A has linearly independent columns, AᵀA is invertible and x* = (AᵀA)⁻¹Aᵀb uniquely.
The matrix P = A(AᵀA)⁻¹Aᵀ is called the projection matrix (or hat matrix in statistics). It satisfies P² = P (applying the projection twice gives the same result) and Pᵀ = P (it is symmetric). These two properties — idempotent and symmetric — completely characterize orthogonal projection matrices. Any time you see a matrix satisfying P² = P and Pᵀ = P, you know it is projecting onto some subspace. Least squares is ubiquitous: it underlies linear regression, Fourier series approximation, and signal processing, wherever you need the best approximation to something you cannot represent exactly.