13 Unsupervised Learning13.1 Unsupervised Learning_ Introduction13.2 K-Means Algorithm13.3 Optimization Objective13.4 Random Initialization13.5 Choosing the Number of Clusters14 Dimensionality Reduction14.1 Motivation I_ Data Compression14.2 Motivation II_ Visualization14.3 Principal Component Analysis Problem Formulation14.4 Principal Component Analysis Algorithm14.5 Reconstruction from Compressed Representation14.6 Choosing the Number of Principal Components14.7 Advice for Applying PCA