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

13 Unsupervised Learning

13.1 Unsupervised Learning_ Introduction

13.2 K-Means Algorithm

13.3 Optimization Objective

13.4 Random Initialization

13.5 Choosing the Number of Clusters

14 Dimensionality Reduction

14.1 Motivation I_ Data Compression

14.2 Motivation II_ Visualization

14.3 Principal Component Analysis Problem Formulation

14.4 Principal Component Analysis Algorithm

14.5 Reconstruction from Compressed Representation

14.6 Choosing the Number of Principal Components

14.7 Advice for Applying PCA