Unsupervised Learning: Clustering and Dimension Reduction

This lecture introduces unsupervised learning, focusing on clustering and dimension reduction. It covers the formalization of unsupervised learning, the search for relevant data partitions, and the concept of dimension reduction. Examples include the MNIST dataset and Isomap for nonlinear dimensionality reduction.