Notes-ML-AndrewNg/week8.md

# 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