Clustering: Unsupervised Learning

This lecture covers the concepts of dimensionality reduction using Principal Component Analysis (PCA) and Kernel PCA, as well as the application of clustering algorithms such as K-means. It also delves into Fisher Linear Discriminant Analysis (LDA) for separating classes in data. The presentation includes detailed explanations of the algorithms, their objectives, and properties, along with practical examples and demonstrations. Additionally, it explores the challenges of handling nonlinear data and the use of spectral clustering for graph-based connectivity. The lecture concludes with insights into the state of machine learning in recent years based on surveys among data scientists.