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This lecture introduces the concept of Principal Component Analysis (PCA) by explaining the formalism of projection through linear maps, followed by exercises on reducing the dimensionality of datasets and constructing projections. The instructor demonstrates how to group data points using a matrix A and discusses the criteria for reducing the infinite choices in projecting data.