MML-14-11: Principal Component Analysis (PCA): Maximum Variance & Minimum Reconstruction Derivations – Practice Quiz

45 worked single-select and multiselect questions on Principal Component Analysis (PCA): Maximum Variance & Minimum Reconstruction Derivations. Cognitive level: Tier 2–4. Prerequisites: Parts 03, 04, 13.

Learning objectives: Center data and compute projected variance; Derive principal directions with constrained optimization; Calculate scores, reconstructions and discarded variance; Prove equivalence of variance and reconstruction objectives; Distinguish subspace uniqueness from basis ambiguity; Diagnose scaling, outliers and unsupervised limitations.

Each item is self-contained, with calculations, model diagnostics and explanations of every distractor. Study references: Mathematics for Machine Learning.