Explain the PAC learning framework and its implications for learnability.
Analyze generalization bounds using Rademacher complexity and VC-dimension.
Implement and evaluate support vector machines and kernel methods.
Apply boosting algorithms such as AdaBoost to improve classifier accuracy.
Design and analyze on-line learning algorithms and their regret bounds.
Extend binary classification to multi-class settings and ranking problems.
