Clustering, PCA, and Anomaly Detection

Learning objectives

• • Describe clustering choices and what distance metric assumptions imply
• • Explain PCA as compression, denoising, and visualization rather than just a formula
• • Choose metrics for anomaly detection when positives are rare

Interview prompts

• • How do you evaluate an anomaly detector with few labels?
• • What does PCA throw away and why does that matter?

Prerequisites