Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.
Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.
Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.
