Graduate Course | Spring 2026 | YU
Instructor: Prof. Iddo Drori
Prerequisites: Machine Learning or Artificial Intelligence or Deep Learning or an equivalent class
| Lecture | Topic | Links |
|---|---|---|
| 1 | Introduction | NotebookLM (infographic, slide deck, etc) Homework 1 Expert Iteration |
| 2 | Multi-arm Bandits, Markov Decision Process | |
| 3 | Reinforcement Learning | |
| 4 | Reinforcement Learning | |
| 5 | Reinforcement Learning | |
| 6 | Deep Reinforcement Learning | |
| 7 | Deep Reinforcement Learning | |
| 8 | Deep Reinforcement Learning | |
| 9 | Multi-Agent Reinforcement Learning | |
| 10 | Multi-Agent Reinforcement Learning | |
| 11 | Games | |
| 12 | Reinforcement Learning and Language Models | |
| 13 | Imitation Learning | |
| 14 | TBD | |
| 15 | Summary |
Participation: 5%
In-class presentations: 30%
Quizzes: 35%
Homeworks: 30%