From Statistics to Machine Learning

Define artificial intelligence and machine learning, and describe their relationship.

Distinguish between supervised, unsupervised, and reinforcement learning paradigms.

Compare the predictive modeling approach of machine learning with traditional econometric methods.

Explain core ML concepts: features, training/testing data, and model evaluation.

Describe the bias-variance tradeoff as a central challenge in predictive modeling.