[TOC]

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