| Class # | Date | Lecture Topic | Reading | Assignments | Resources, and Additional Readings |
| Week 1 | |||||
| 1 | 8/25 | Intro & History of AI | Ch 1 | ||
| 2 | 8/27 |
Agents
|
Ch. 2 | ||
| Week 2 | |||||
| 3 | 9/1 |
Uninformed Search: Tree Search, BFS, DFS | Sec. 3.1-3.2 | ||
| 4 | 9/3 | Uninformed Search: Informed Search: Greedy, A* | Sec. 3.1-3.2 | ||
| Week 3 | |||||
| 5 | 9/8 |
Constraint Satisfaction Problems
|
Ch. 6 | ||
| 6 | 9/10 |
Constraint Satisfaction Problems | Ch. 6 | ||
| Week 4 | |||||
| 7 | 9/15 | Logical Agents | Ch. 7 | ||
| 8 | 9/17 | Logical Agents | Ch. 7 | ||
| Week 5 | |||||
| 9 | 9/22 | Adversarial Search | Ch. 5 | ||
| 10 | 9/24 | Adversarial Search | Ch 5 | ||
| Week 6 | |||||
| 11 | 9/29 | Probability |
Ch 13 | ||
| 12 | 10/1 | Probability
| Ch 13 | ||
| Week 7 | |||||
| 13 | 10/6 | Bayesian Inference and Bayesian Learning | Ch 13 | ||
| 14 | 10/8 | Decision Theory | Sec 16.1-16.3 | ||
| Week 8 | |||||
| 15 | 10/13 | Markov Decision Process | Ch 17 | ||
| 16 | 10/15 | Reinforcement Learning |
Ch 21 | ||
| Week 9 | |||||
| 17 | 10/20 | Reinforcement Learning
| Ch 21 | ||
| 18 | 10/22 | Reinforcement Learning | Ch 21 | ||
| Week 10 | |||||
| 19 | 10/27 | Midterm |
Ch 21 | ||
| 20 | 10/29 | Machine Learning: Intro and Decision Trees | Ch 18 | ||
| Week 11 | |||||
| 21 | 11/3 | Machine Learning: Decision Trees |
Ch 18 | ||
| 22 | 11/5 | Machine Learning: Linear Regression | Ch 18 | ||
| Week 12 | |||||
| 23 | 11/10 | Machine Learning: Artificial Neural Networks | Ch 18 | ||
| 24 | 11/12 | Machine Learning: Non parametric models |
Ch 18 | ||
| Week 13 | |||||
| 25 | 11/17 | Hidden Markov Models | Ch 15 | ||
| 26 | 11/19 | Hidden Markov Models |
Ch 15 | ||
| Week 14 | |||||
| 27 | 11/24 | Hidden Markov Models | Ch 15 | ||
| 28 | 11/26 | Thanksgiving
| Sec 7.5 | ||
| Week 15 | |||||
| 29 | 12/3 | ||||
| 29 | 12/5 | ||||