# Week 13 β Unsupervised (11/14β18)

## Contents

# Week 13 β Unsupervised (11/14β18)#

In this week, we are going to talk more about *unsupervised learning* β learning without labels.
We are not going to have time to investigate these techniques very deeply, but I want you to know about them, and you are experimenting with them in Assignment 6.

This weekβs content is lighter, since we just had a large assignment and a midterm, and another assignment is due on Sunday.

## π§ Content Overview#

Element |
Length |
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2m51s | |

17m22s | |

6m56s | |

7m27s | |

10m31s |

This week has **0h45m** of video and **0 words** of assigned readings. This weekβs videos are available in a Panopto folder.

## π Deadlines#

Quiz 13, November 17

Assignment 6, November 20

## π₯ No Supervision#

In this video, we review the idea of supervised learning and contrast it with unsupervised learning.

## π₯ Decomposing Matrices#

This video introduces the idea of *matrix decomposition*, which we can use to *reduce the dimensionality* of data points.

### Resources#

The next notebook

The PCADemo, demonstrating the PCA plots

## π Movie Decomposition#

The Movie Decomposition notebook demonstrates matrix decomposition with movie data.

## π₯ Clustering#

This video introduces the concept of *clustering*, another useful unsupervised learning technique.

### Resources#

## π Clustering Example#

The clustering example notebook shows how to use the `KMeans`

class.

## π₯ Vector Spaces#

This video talks about *vector spaces* and transforms.

### Resources#

Linear Algebra Done Right by Sheldon Axler

Handbook of Linear Algebra (terse and comprehensive reference)

## π₯ Information and Entropy#

This video introduces the idea of *entropy* as a way to quantify information. Itβs something I want to make sure youβve seen
at least once by the end of the class.

### Resources#

An Introduction to Information Theory: Symbols, Signals & Noise by John R. Pierce

Entropy (information theory) on Wikipedia

## π© Week 13 Quiz#

Take the Week 13 quiz on Canvas.

## π Practice: SVD on Paper Abstracts#

The Week 13 Exercise notebook demonstrates latent semantic analysis on paper abstracts and has an exercise to classify text into new or old papers.

It requires the `chi-papers.csv`

file, which is derived from the HCI Bibliography.
It is the abstracts from papers published at the CHI conference (the primary conference for human-computer interaction) over a period of nearly 40 years.

If you want to see how to create this file, see the Fetch CHI Papers example.

## π© Assignment 6#

Assignment 6 is due **November 20**.