WEEK 9: UNSUPERVISED LEARNING

Clustering algorithms including k-means, GMM, hierarchical and density-based clustering

November 10, 2025

← All Weeks

Weekly Materials

🎓

Lecture Slides

Professor's main lecture presentation
👩‍💻

TA Practice Slides

Hands-on tutorials and practice exercises

→
📖

Lesson Guide

Comprehensive explanations and theory

→
💻

Code Examples

K-Means Car Dataset GMM Example DBSCAN Example Hierarchical Clustering PCA with Iris Dataset
📚

References & Resources

ISL Ch. 10 Unsupervised Learning
PML Ch. 10.1–10.4 PCA as latent factor model
PML Ch. 11.1–11.3 Clustering, mixture models, EM algorithm

Additional Notes

Week 9: Unsupervised Learning

Topics Covered

  • k-Means Clustering
  • Gaussian Mixture Models (GMM)
  • Expectation Maximization
  • Hierarchical Clustering
  • Density-based Clustering (DBSCAN)

Code Examples

This week features multiple clustering implementations including k-means, GMM, DBSCAN, and hierarchical clustering with real datasets.

Further Reading

  • ISL Ch. 10: Unsupervised Learning
  • PML Ch. 10.1–10.4: PCA as latent factor model
  • PML Ch. 11.1–11.3: Clustering, mixture models, EM algorithm