Graduate Course | Spring 2026 | YU
Instructor: Prof. Iddo Drori
Time: Monday, 5:30-7:30pm
Room: 700
Office Hours: Thursday 1-2pm, Room 700
Prerequisites: Machine Learning or Artificial Intelligence or Deep Learning or an equivalent class
| Lecture | Date | Topic | Links |
|---|---|---|---|
| Tuesday, January 20 | First day of classes | ||
| 1 | Monday, January 26 | Introduction | NotebookLM (infographic, slide deck, etc) Homework 1 Expert Iteration |
| 2 | Monday, February 2 | Multi-arm Bandits, Markov Decision Process | |
| 3 | Monday, February 9 | Reinforcement Learning | |
| 4 | Monday, February 16 | Reinforcement Learning | |
| 5 | Monday, February 23 | Reinforcement Learning | |
| Monday, March 2 | Purim, no classes after 1pm | ||
| Tuesday, March 3 | Purim, no classes, University closed | ||
| 6 | Monday, March 9 | Deep Reinforcement Learning | |
| 7 | Monday, March 16 | Deep Reinforcement Learning | |
| 8 | Monday, March 23 | Deep Reinforcement Learning | |
| 9 | Monday, March 30 | Multi-Agent Reinforcement Learning | |
| Wednesday, April 1 - Thursday, April 9 | Passover Spring Break (University closed, no classes) | ||
| 10 | Monday, April 13 | Multi-Agent Reinforcement Learning | |
| 11 | Monday, April 20 | Games | |
| 12 | Monday, April 27 | Reinforcement Learning and Language Models | |
| 13 | Monday, May 4 | Imitation Learning | |
| 14 | Monday, May 11 | TBD | |
| 15 | Monday, May 18 | Summary (Last day of classes) |
Participation: 5%
In-class presentations: 30%
Quizzes: 35%
Homeworks: 30%