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- | ====== Schedule ====== | + | ===== Schedule ====== |
===== Announcements ===== | ===== Announcements ===== | ||
+ | Links to live MS Teams events: | ||
+ | * Lectures: [[https:// | ||
+ | * Office hours: Apoorv [[https:// | ||
+ | * Office hours: Chaitanya [[https:// | ||
+ | * Office hours: Chuck [[https:// | ||
+ | |||
- | Lecture | + | Recordings of lecture |
[[https:// | [[https:// | ||
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^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 2:\\ Aug 31 - Sept 4 | Problem-solving search and how to measure performance.\\ Iterative deepening and other uninformed search methods. | + | | Week 2:\\ Aug 31 - Sept 4 | Help with A1.\\ Problem-solving search and how to measure performance.\\ Iterative deepening and other uninformed search methods. |
- | | Week 3:\\ Sept 7 - Sept 11 | Informed search. A* search. Python | + | | Week 3:\\ Sept 7 - Sept 11 | Informed search. A* search. Python classes, sorting, numpy arrays. |
- | | Week 4:\\ Sept 14 - Sept 18 | A* optimality, admissible heuristics, effective branching factor.\\ Local search and optimization. | + | | Week 4:\\ Sept 14 - Sept 18 | A* optimality, admissible heuristics, effective branching factor.\\ Local search and optimization. |
| Week 5:\\ Sept 21 - Sept 25 | Adversarial search. Minimax. Alpha-beta pruning. Stochastic games. | | Week 5:\\ Sept 21 - Sept 25 | Adversarial search. Minimax. Alpha-beta pruning. Stochastic games. | ||
| Week 6:\\ Sept 28 - Oct 2 | Negamax, with pruning. Introduction to Reinforcement Learning. | | Week 6:\\ Sept 28 - Oct 2 | Negamax, with pruning. Introduction to Reinforcement Learning. |