The Perceptron
Description: We will continue exploring 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.Department: Centro de Estudios y Asesorías en Estadística (CEASE)
Institution: Universidad de Nariño
Date: June 21, 2025
Hours: 4
From: 8:30 am
To: 12:00 am
Resources
Books
- Russell, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach 3rd ed - Chapter 19 . Prentice Hall
- Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer - Chapter 4
- Deisenroth M. P. et. al. (2020). Mathematics for Machine Learning - Chapter 10
Papers and Reports
- McCulloch, W., Pitts, W. (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity
- Rosenblatt, F. (1962). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms
- Minsky, M., Papert, S. (1969). Perceptrons: An Introduction to Computational Geometry. MIT Press.
Web
- Perceptron Learning Algorithm
- Neural Networks and Deep Learning by Michael Nielsen
- 3Blue1Brown Neural Networks Series