Delves into model evaluation, covering theory, training error, prediction error, resampling methods, and information criteria.
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.
Introduces machine learning basics, including data collection, model evaluation, and feature normalization.
