Skip to main content Accessibility help
Internet Explorer 11 is being discontinued by Microsoft in August 2021. If you have difficulties viewing the site on Internet Explorer 11 we recommend using a different browser such as Microsoft Edge, Google Chrome, Apple Safari or Mozilla Firefox.

Chapter 10: Unsupervised Learning

Chapter 10: Unsupervised Learning

pp. 290-318

Authors

, University of Washington
Resources available Unlock the full potential of this textbook with additional resources. There are free resources and Instructor restricted resources available for this textbook. Explore resources
  • Add bookmark
  • Cite
  • Share

Summary

In the previous chapter, we saw how to learn from data when the labels or true values associated with them are available. In other words, we knew what was right or wrong and we used that information to build a regression or classification model that could then make predictions for new data. Such a process fell under supervised learning. Now, we will consider the other big area of machine learning where we do not know true labels or values with the given data, and yet we will want to learn the underlying structure of that data and be able to explain it. This is called unsupervised learning.

About the book

Access options

Review the options below to login to check your access.

Purchase options

eTextbook
US$61.00
Hardback
US$61.00

Have an access code?

To redeem an access code, please log in with your personal login.

If you believe you should have access to this content, please contact your institutional librarian or consult our FAQ page for further information about accessing our content.

Also available to purchase from these educational ebook suppliers