Machine Learning
Home
FAQ
Syllabus
Resources
Contact
Google Group
Lectures
1 Introduction
2 Basic Statistics
3 Instance based methods
4 Perceptron
5 Support Vectors
6 Kernels
7 Kernels
8 Stochastic Convergence
9 Tail Bounds
10 Risk Minimization
11 Learning Theory
12 Gaussian Processes
13 Exponential Families
14 PCA
15 Graphical Models
16 Dynamic Programming
17 Latent Variables I
18 Latent Variables II
19 EM and Clustering
20 ICA
21 Sampling
21 Trees
22 Neural Networks
23 Boosting
24 Reinforcement Learning
25 Scalability
Projects
Recitations
1 Statistics
2 Linear Algebra
3 Optimization
4 Model Selection
5 Convergence Bounds
6 Kernels
7 Risk. Gaussian Processes
8 Exponential Families
9 Information Theory
CMU
Alex Smola
Barnabas Poczos
Ina Fiterau
Junier Oliva
Leila Wehbe
Mu Li
Xuezhi Wang
MLD
CMU

Principal Component Analysis

Introduction to Machine Learning - 10-701/15-781

Content

  • Principal Components

  • PCA algorithms

  • Applications

    • Eigenfaces

    • Image compression

    • Image denosing

Supplementary material

Slides in PDF

Videos

This is unedited video straight from a Lumix GF2 with a 20mm lens which should explain the sound (it doesn't have a dedicated audio input) … But it should help as a supplement with the slides (YouTube typically makes the 1080i version available within 1 week of the upload).