Delves into the trade-off between model complexity and risk, generalization bounds, and the dangers of overfitting complex function classes.
Covers boosting with a focus on the Adaboost algorithm, forward stagewise additive modeling, and gradient tree boosting.
Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
