Unsupervised Learning

Week 11, Fall 2023

Summary

This week will introduce unsupervised learning. We will look at a variety of methods for the various subtasks: dimension reduction, clustering, density estimation, and outlier detection.

Learning Objectives

After completing this week, you are expected to be able to:

  • Understand the difference between supervised and unsupervised machine learning tasks.
  • Identify supervised and unsupervised machine learning tasks.
  • Understand and identify unsupervised learning subtasks: dimension reduction, clustering, density estimation, and outlier detection.
  • Use principal components analysis (PCA) for dimension reduction.
  • Use k-means and hierarchical clustering for clustering.
  • Use kernel density estimation and mixture models for density estimation.
  • Use one-class SVM and isolation forest for outlier detection.

Reading

Link Source
Week 11 Concept Scribbles Course Website
Week 11 Notebook [ Rendered Notebook ] Course Website
Density Estimation and Clustering IDS 705 @ Duke

Video

Head to ClassTranscribe to watch lecture recordings. They are arranged by date in the Lecture Capture Recordings playlist.

Assignments

Assignment Deadline Credit
Lab 08 [ Template ] Thursday, November 9 100% / 105%
Homework 08 Thursday, November 9 105%

Office Hours

Staff Day Time Location
David Monday 11:00 AM - 12:00 PM 2328 Siebel Center
Lahari Wednesday 4:00 PM - 5:00 PM Siebel Center, Second Floor [ Queue ]
David Wednesday 5:00 PM - 6:00 PM Zoom
Eunice Thursday 3:00 PM - 4:00 PM Siebel Center, Second Floor [ Queue ]