Principal Component Analysis (PCA)

Key concepts:

• Covariance matrix, eigenvectors/values, explained variance.
• Centering/scaling; when PCA helps or hurts.
• Interpreting loadings; whitening.

Common interview checks:

• PCA vs. autoencoders; leakage risks.
• How many components to keep; scree plots.