Machine Learning BasicsIntroduces the basics of machine learning, covering supervised classification, decision boundaries, and polynomial curve fitting.
Generalization TheoryExplores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.
Bias-Variance Trade-OffExplores underfitting, overfitting, and the bias-variance trade-off in machine learning models.
Model EvaluationExplores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.
Nonlinear ML AlgorithmsIntroduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.