Week 7: Semi-Supervised and Reinforcement Learning
Dates: Feb 23-27 · Reading: Handout 6: Semi-Supervised and Reinforcement Learning
Learning Objectives
- Explain semi-supervised learning as learning from labeled and unlabeled data
- Describe reinforcement learning as learning from rewards and penalties
- Identify security use cases for semi-supervised and reinforcement learning
- Relate RL to defender-attacker game theory
Monday Session
Semi-supervised learning: a few labeled examples and lots of unlabeled data. Reinforcement learning intuition: learning from rewards and penalties. Security games and RL in defender-attacker scenarios.
Wednesday Session
Semi-supervised concepts applied to malware classification and intrusion detection. Reinforcement learning in security: game theory, defender-attacker interactions, and automated response strategies.
Lab
Lab 6: Semi-Supervised Classification. Classify malware with limited labeled data and see how unlabeled data helps when labels are scarce.
Quiz / This Week
Quiz 6. Semi-supervised learning; reinforcement learning; learning paradigms; game theory in security.