Introduction: classification by a simple perceptron

This lecture introduces the concept of a simple perceptron, which is a single-layer network implementing a hyperplane in the input space. The instructor explains the geometry of the perceptron, the critical cases, and the process of removing the threshold by adding a constant input. Additionally, the lecture covers how a simple perceptron can solve linearly separable problems by imposing a separating hyperplane and adapting the weight vector through learning.