Overview
Unsupervised learning focuses on discovering structure in data without labeled outcomes. In this block we examine clustering methods, dimensionality reduction techniques, and feature extraction.
Topics Covered
- Clustering: K-Means, Hierarchical Clustering, DBSCAN
- Dimensionality Reduction: PCA, t-SNE
- Feature Extraction and Selection
- Practical applications of Unsupervised Learning
Resources
Key Topics, Meeting Notes / Lesson Objectives, and Assignments
| Date | Key Topics | Meeting Notes and Lesson Objectives | Assignments |
|---|---|---|---|
| 07 Apr 2026 | Lesson 12: Unsupervised Learning 1 | Overview of clustering and PCA. Understand when unsupervised methods are appropriate and how they differ from supervised approaches. | ISLP 503-534; MLP Mod 4 |
| 14 Apr 2026 | Lesson 13: Unsupervised Learning 2 | Compare clustering algorithms (K-Means, Hierarchical, DBSCAN) and discuss distance metrics, cluster assumptions, and evaluation methods. | Run K-Means on a dataset of your choice and visualize results. Experiment with different numbers of clusters. |
| 21 Apr 2026 | Lesson 14: Project 3 Practical Unsupervised Model 1 | Understand variance maximization (PCA) and nonlinear manifold learning (t-SNE). | Peform Unsupervised learning on selected dataset. Complete Report |
| 28 Apr 2026 | Lesson 15: Project 3 Practical Unsupervised Model 2 | Turn in and Present unsupervised learning project 3. | Demonstrate unsupervised learning on selected dataset in front of class. |
| 5 May 2026 | Lesson 16: Regularizatoin | Review regularization techniques and explore uses. | ISLP 229-252; MLP Mod 5 |