Nonlinear ML AlgorithmsIntroduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Generalization in Deep LearningDelves into the trade-off between model complexity and risk, generalization bounds, and the dangers of overfitting complex function classes.
Neural Networks: Multilayer PerceptronsCovers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Cross-validation & RegularizationExplores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.
Linear Models and OverfittingExplores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.
