These notes cover the core supervised learning paradigm where models learn from labeled data:
- Regression: Linear regression, polynomial regression, cost functions, and gradient-based optimization.
- Classification: Logistic regression, decision boundaries, softmax, and multi-class classification.
- Model Evaluation: Train/test splits, cross-validation, precision, recall, F1 score, and confusion matrices.
- Regularization: L1/L2 penalties, overfitting vs underfitting, and the bias-variance tradeoff.