Foundations of Machine Learning

Distinguish between supervised, unsupervised, and reinforcement learning paradigms.

Compare classification versus regression tasks with concrete examples.

Define loss functions and explain their role in model training.

Explain gradient descent as a method for optimizing model parameters.

Explain the bias-variance tradeoff and its implications for model performance.

Describe the purpose and mechanics of train-test-validation data splits for model evaluation.