19th Century

The Early History of Machine Learning

In the 19th century, key ideas emerged that later became the foundation for the development of machine learning. Some scientists developed methods for data processing and statistical analysis, such as the method of least squares and Bayesian inference. During this period, the first designs for programmable machines also appeared, foreshadowing modern computers.

Key Events

• 1805: Adrien-Marie Legendre and Carl Friedrich Gauss independently developed the method of least squares, which became a fundamental technique in statistical learning and regression analysis.

• 1812: Pierre-Simon Laplace published "Théorie analytique des probabilités," which laid the groundwork for Bayesian inference, a key concept in many machine learning algorithms.

• 1834: Charles Babbage, often called the father of computers, designed a machine that could be programmed using punch cards. Although the machine was never built, it laid the groundwork for the logical structure of all modern computers.

• The beginning of the application of mathematical methods to solving practical problems, which laid the foundation for many modern machine learning and statistics algorithms.