Lectures for Artificial Intelligence at the university of Aberdeen.
| Week | Lecture | Reading | Practical |
|---|---|---|---|
| 8 | 1. Intro | AIMA Ch 1 | None |
| 2. Agents | AIMA Ch 2 | ||
| 9 | 3. Search 1: Uninformed Search | AIMA Ch 3.1-3.5 | Agents |
| 4. Search 2: Heuristic Search | AIMA Ch 3.5-3.6 | ||
| 10 | 5. Search 3: Local Search | AIMA Ch 4.1-4.4 | Search and Heuristics |
| 6. Search 4: Adversarial Search | AIMA Ch 5 | ||
| 11 | 7. Reasoning 1: Constraint Satisfaction | AIMA Ch 6 | Local and Adversarial Search |
| 8. Reasoning 2: Logic and Inference | AIMA Ch 7.1-7.5 and 9 | ||
| 12 | 9. Probabilistic Reasoning 1: Bayesian Networks | AIMA Ch 12-13 | CSP and SAT |
| 10. Probabilistic Reasoning 2: HMMs | AIMA Ch 14.1-14.3 | ||
| 13 | 11. Planning 1: Intro and Formalism | AIMA Ch 11.1 IPDDL 2.1-2.2 |
HMMs |
| 12. Planning 2: Algorithms and Heuristics | AIMA Ch 11.2-11.3 APTP 9.1-9.3 |
||
| 14 | 13. Planning 3: Hierarchical Planning | AIMA Ch 11.4 APTP 11.1-11.4 |
Planning Modelling |
| 14. Planning 4: Stochastic Planning | AIMA Ch 17 | ||
| 15 | 15. Learning 1: Intro to ML (Decision Trees) | AIMA Ch 19.1-19.3 | HTNs and MDPs |
| 16. Learning 2: Regression | AIMA Ch 19.4-19.6 | ||
| 16 | 17. Learning 3: Neural Networks | AIMA Ch 21 | Decision Trees |
| 18. Learning 4: Reinforcement Learning | AIMA Ch 22 | ||
| 17 | 19. Ethical Issues in AI | AIMA Ch 27 | Reinforcement Learning |
| 20. Conclusions and Discussion | AIMA Ch 28 | ||
| 18 | Reading Week | None | None |
| 19 | Assessment | None | None |
| Week | Assessment Description |
|---|---|
| 12 | Programming Assignment Starts |
| 18 | Programming Assignment Due |
| 19 | Exam |
| 21 | Programming Assignment Feedback |
