Unsupervised Learning
Week 11, Fall 2023
- Start: Monday, October 30
- End: Friday, November 3
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 ] |