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Applications in Unsupervised Learning

PPOL 670 | Introduction to Data Science

Lecture Materials for Week 13

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Learning Objectives

In the Asynchronous Lecture

• Refresher on what unsupervised learning is and how it differs from supervised learning.
• Discuss how the K-Means Clustering algorithm works
• Explore how Hierarchical Clustering works

In the Synchronous Lecture

• Walk through implementing unsupervised learning methods to classify regime time.
• Discuss rules of thumb for data exploration in the social sciences when we have weak priors on the data being explored.

If you have any questions while watching the pre-recorded material, be sure to write them down and to bring them up during the synchronous portion of the lecture.

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Asynchronous Materials

The following tabs contain pre-recorded lecture materials for class this week. Please review these materials prior to the synchronous lecture.

Total time: Approx. 34 Minutes

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Unsupervised Learning

Relevant Slides

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K-Means Clustering

Relevant Slides

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Hierarchical Clustering

Relevant Slides

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The following materials were generated for students enrolled in PPOL670. Please do not distribute without permission.

ed769@georgetown.edu | www.ericdunford.com