Course Schedule
The schedule is tentative and subject to change based on the pace of progress, student feedback, or unforeseen circumstances.
| Event | Week | Description | Required reading |
|---|---|---|---|
| Lecture | 1 | Introduction to AI | RN (Russell & Norvig) Chp 1, 2 |
| Assignment | 1 | A0: Tutorial | |
| Lecture | 2 | Uniformed Search | RN Chp 3.1-3.4 |
| Assignment | 2 | A1: Search | |
| Lecture | 2 | Informed Search | RN Chp 3.5-3.6 |
| Lecture | 3 | Game Trees | RN Chp 5.2-5.3 |
| Lecture | 3 | Expectimax | RN Chp 5.4-5.5 |
| Contest | 3 | C1: Multiagent Search | |
| Lecture | 4 | Algoritmic Game Theory | NR Chp 17.5 |
| Assignment | 4 | A2: Adversarial Planning | |
| Contest | 4 | C2: Game Theory | |
| Lecture | 5 | Markov Decision Processes | RN Chp 17.1-17.3 |
| Lecture | 6 | Reinforcement Learning | RN Chp 21 |
| Assignment | 6 | A3: Reinforcement Learning | |
| Lecture | 7 | Bayesian Networks | RN Chp 13.1-13.5, 14.1-14.4 |
| Lecture | 8 | Decision Networks | RN Chp 15.2-15.5 |
| Lecture | 8 | Hidden Markov Models | RN Chp 16.5-16.6 |
| Exam | 9 | Midterm Exam | |
| Lecture | 10 | Particle Filtering | RN Chp 15.2-15.6 |
| Assignment | 10 | A4: Bayes Nets and HMMs | |
| Lecture | 10 | Naive Bayes | RN Chp 20.1-20.2 |
| Lecture | 11 | Decision Trees | RN Chp 18.3 |
| Lecture | 11 | Perceptrons | RN Chp 18.6 |
| Assignment | 11 | A5: Machine Learning | |
| Lecture | 12 | Neural Networks | RN Chp 18.8 |
| Contest | 12 | C3: Capture the Flag | |
| Lecture | 13 | Constraint Satisfaction Problems | RN Chp 6.1-6.2, 6.5 |
| Lecture | 14 | Advance Topics TBD |