Unsupervised Learning (Part 1)

Abstract: This first session introduces the motivations and foundational methods for dimensionality reduction under unsupervised learning. We begin by discussing why dimension reduction matters - especially in high-dimensional data settings - and how it helps address issues like the "curse of dimensionality," multicollinearity, overfitting, and challenges in visualization and interpretation. Then we focus on Principal Component Analysis (PCA): its mathematical foundations, how it identifies dominant modes of variation, how to interpret the principal components, and how to choose the number of components.

Computer scienceMathematics

Audience: general audience

Basic Notions and Applied Topology Seminar