ARTIFICIAL INTELLIGENCE (4 Credits)

Learning Outcomes:

On successful completion of this Course, students will be able to: LO1 – describe the concept of the intelligent agent and Artificial Intelligence; LO2 – apply various search algorithms for agents to solve problems; LO3 – apply various knowledge representations for reasoning purpose; LO4 – apply various techniques to an agent when acting under uncertainty; LO5 – apply various learning algorithms for agents to solve problems; LO6 – analyze the impact of AI in society concerning ethical, fairness, and safety issues.

Topics:

1. Introduction to Artificial Intelligence;
2. Local Search;
3. Adversarial Search;
4. Intelligent Agents;
5. Logical Agents;
6. First Order Logic;
7. Quantifying Uncertainty;
8. Bayesian Network;
9. Bayesian Network: Exact Inference;
10. Markov Model: Markov Chain, HMM;
11. Review and Discussion I;
12. Introduction to Machine Learning;
13. Philosophy, Ethics, and Safety of AI;
14. Linear Regression;
15. Decision Tree;
16. K-Nearest Neighbor;
17. Neural Networks;
18. K-Means;
19. Review and Discussion II;
20. Search: State Space, Uninformed Search;
21. Search: Heuristics, Informed Search;
22. Project Discussion I;
23. Fuzzy Set;
24. Fuzzy Logic;
25. Markov Decision Process.