Introduction to Machine Learning

Distinguish between supervised, unsupervised, and reinforcement learning

Explain the bias-variance tradeoff

Split data into training, validation, and test sets

Apply cross-validation techniques for model evaluation

Describe common evaluation metrics for regression (e.g., MSE, R²) and classification (e.g., accuracy, precision, recall)

Identify overfitting and underfitting by comparing training and validation performance

Describe common strategies to mitigate overfitting and underfitting