Introduction to Supervised Learning: Classification and Perceptrons

This lecture introduces supervised learning, focusing on classification as a geometric problem. The instructor explains how input vectors are transformed by classifiers, such as neural networks, to produce binary outputs. The lecture emphasizes the process of converting images into vectors and the concept of finding a separating surface in high-dimensional input space for classification tasks. The instructor also discusses discriminant functions and linearly separable problems.