hese materials provide an introduction to the theory behind artificial intelligence learning, specifically focusing on supervised learning and decision trees. The first source covers fundamental concepts like the distinction between training and test error, the importance of the IID assumption, the trade-off between model complexity and generalization, and techniques for evaluating and selecting models using validation and cross-validation. The second source offers a simplified analogy and steps for building and validating a decision tree for a car charging location prediction task. The third source explains how decision trees function as a series of if-else rules and demonstrates the process of learning a simple one-level decision tree (decision stump) from data by optimizing a scoring function like classification accuracy.