Dimensionality Reduction: PCA & t-SNE

This lecture covers the concepts of Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction. PCA focuses on finding the directions of maximum variance in the data, while t-SNE aims to preserve the topology of the data in a low-dimensional space. The lecture explains the mathematical principles behind these methods and their applications in visualizing high-dimensional data.