Mathematical Paradigms: Supervised versus Unsupervised Learning

Machine learning is fundamentally a optimization problem. In supervised learning, algorithms minimize empirical risk using labeled target vectors. Unsupervised learning, conversely, uncovers hidden density structures within unlabeled input matrices. This article details the optimization mathematics behind Support Vector Machines (SVM), kernel tricks, k-means clustering, and Principal Component Analysis (PCA).