Machine Learning for Archaeological Applications in R

This Element highlights the employment within archaeology of classification methods developed in the field of chemometrics, artificial intelligence, and Bayesian statistics.

These run in both high- and low-dimensional environments and often have better results than traditional methods.

Instead of a theoretical approach, it provides examples of how to apply these methods to real data using lithic and ceramic archaeological materials as case studies.

A detailed explanation of how to process data in R (The R Project for Statistical Computing), as well as the respective code, are also provided.