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

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