From Linear Models to the Perceptron

This lecture explores the idea of learning from data. We explore the induction process that agents follow to go from a specific set of observations to general rules. We will explore linear regression and classification models, building upon these foundations to introduce the perceptron as a fundamental building block for neural networks. We examine their mathematical formulation, learning process, and limitations, concluding with how these simple units form the basis for neural network architectures.