Flexibility of Models & Bias-Variance Trade-Off

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.

Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.

Explores underfitting, overfitting, and the bias-variance trade-off in machine learning models.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.