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 – Explain various intelligent search algorithms to solve the problems; LO3 – Apply various techniques to an agent when acting under certainty; LO4 – Apply various learning algorithms to solve the problems; LO5 – Analyze the role of Ethical in Artificial Intelligence.

Topics:

1. Principal Component Analysis;
2. Proposal Presentation;
3. Fuzzy Logic;
4. Search Strategies;
5. Introduction to Artificial Intelligence;
6. K-Nearest Neighbour;
7. Local Search;
8. Fuzzy Set; K-Means;
9. Review;
10. Markov Model and Hidden Markov Model;
11. Support Vector Machine;
12. Project Presentation;
13. Linear Regression;
14. Adversarial Search;
15. Bayesian Network;
16. Introduction to Neural Network and Deep Learning;
17. Quantifying Uncertainty;
18. Introduction to Bayesian Network;
19. Decision Tree;
20. Intelligent Agents;
21. Application and Ethics in Artificial Intelligence;
22. Introduction to Machine Learning.