Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.