Image Classification: Decision Trees & Random Forests

This lecture covers the principles of image classification using decision trees and random forests. It explains how decision trees segment the input space and how random forests reduce variance by building a committee of models. The instructor discusses the construction of decision trees, the importance of variable selection, and the process of training a random forest classifier. Practical examples and exercises on training and predicting with random forests are also provided.