WEEK 9: UNSUPERVISED LEARNING

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

November 10, 2025

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Weekly Materials

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Lecture Slides

Professor's main lecture presentation

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TA Practice Slides

Hands-on tutorials and practice exercises

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Lesson Guide

Comprehensive explanations and theory

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Code Examples

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

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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