Unsupervised Learning

Clustering

Unsupervised Learning

K-Means Algorithm

Optimization Objective

Random Intialization

Choosing the Number of Clusters

Review

Dimensionality Reduction

Motivation

Motivation I: Data Compression

Motivation II: Visualization

Principal Component Analysis

Principal Component Analysis Problem Formulation

Principal Component Analysis Algorithm

Applying PCA

Reconstruction from Compressed Representation

Choosing the Number of Principal Components

Advice for Applying PCA

Review