Week 13 — Unsupervised (11/15–19)¶
In this week, we are going to talk more about unsupervised learning — 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 week’s content is lighter, since we just had a large assignment and a midterm, and another assignment is due on Sunday.
🧐 Content Overview¶
🎥 No Supervision¶
In this video, we review the idea of supervised learning and contrast it with unsupervised learning.
🎥 Decomposing Matrices¶
This video introduces the idea of matrix decomposition, which we can use to reduce the dimensionality of data points.
📓 Movie Decomposition¶
The Movie Decomposition notebook demonstrates matrix decomposition with movie data.
This video introduces the concept of clustering, another useful unsupervised learning technique.
🎥 Vector Spaces¶
This video talks about vector spaces and transforms.
🎥 Information and Entropy¶
This video introduces the idea of entropy as a way to quantify information. It’s something I want to make sure you’ve seen at least once by the end of the class.
An Introduction to Information Theory: Symbols, Signals & Noise by John R. Pierce
Entropy (information theory) on Wikipedia
🚩 Week 13 Quiz¶
Take the Week 13 quiz on Canvas.
📓 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.