Lecture Notes

Lecture 1: Introduction, Jan 26
Lecture 2: Intelligent Agents, Jan 28, 2020
Lecture 3: Search, Feb 2, 2020
Lecture 4: Graph Search, Feb 4, 2020
Presentation Lab 1: Search, Feb 9, 2020
Lecture 6: Informed Search, Feb 11, 2020
Lecture 7: Adversarial Search, Feb 16, 2020
Lecture 8: Monte Carlo Tree Search, Feb 18, 2020
Presentation Lab 2: MCTS, Feb 23, 2020
Lecture 10: Logic, Feb 25, 2020
Lecture 11: Logic: Implementation and Actions, Mar 2, 2020
Lecture 12: Planning, Mar 4, 2020
Lecture 13: PDDL, Mar 9, 2020
Lecture 14: Applications of Logic, Mar 11, 2020
Lecture 15: Review, Mar 16, 2020
Presentation Lab 3: Planning, Mar 23, 2020
Lecture 17: Machine Learning, Mar 25, 2020
Lecture 18: Reinforcement Learning, Apr 6, 2020
Lecture 19: Supervised Learning, Apr 8, 2020
Lecture 20: Neural Networks, Apr 13, 2020
Lecture 21: Neural Networks in PyTorch, Apr 15, 2020
Lecture 22: Generative Adversarial Networks, Apr 20, 2020
Presentation Lab 4: Generative Adversarial Networks, Apr 22, 2020
Lecture 24: Advanced Neural Networks, Apr 27, 2020
Lecture 25: Deep Q Learning, Apr 29, 2020
Lecture 26: Ethical Considerations, May 4, 2020
Lecture 27: AI and Humans, May 6, 2020
Lecture 28: Conclusion, May 11, 2020
Lecture 29: Review, May 13, 2020