Unit 2 · Unsupervised Learning

# Syllabus

• Clustering: K-means/Kernel K-means. Dimensionality Reduction: PCA and kernel PCA. Matrix Factorization and Matrix Completion. Generative Models (mixture models and latent factor models)

4 syllabus lines → 11 topics: each line is split into exam-sized topics (clustering → 2.1–2.3), feature selection and the curse of dimensionality get their own space, and 1 PYQ topic (recommender systems) comes from past papers.

# Topics

• 2.1Clustering: Concepts, Requirements & Types★
• 2.2K-means Clustering★★★
• 2.3Kernel K-means☆
• 2.4Dimensionality Reduction & the Curse of Dimensionality★★★
• 2.5Feature Selection★★
• 2.6Principal Component Analysis (PCA)★★★
• 2.7Kernel PCA★
• 2.8Recommender Systems: Content-Based & Collaborative Filtering★★ PYQ
• 2.9Matrix Factorization & Matrix Completion★★
• 2.10Generative Models: Mixture Models (GMM & EM)★★
• 2.11Latent Factor Models☆