Representation Learning Part 2 - MLT 1.4 (Week1 Chapter4) Quiz

This quiz tests your understanding of the core concepts of Principal Component Analysis (PCA), a fundamental technique in machine learning and data science for dimensionality reduction. It covers the mathematical and geometric foundations of PCA, including reconstruction error minimization, variance maximization, the role of the covariance matrix, and the spectral theorem. Questions explore topics such as projection onto principal components, the significance of eigenvectors and eigenvalues, the necessity of normalization constraints, and the handling of real-world data with noise. Designed for learners with a background in linear algebra and machine learning, this quiz challenges you to apply theoretical knowledge to practical PCA scenarios, including optimization problems and geometric interpretations.