ARTIFICIAL INTELLIGENCE (5 Credits)

Learning Outcomes:

On successful completion of this course, student will be able to: identify value propositions in business with physical / visual representation of an idea; describe what is AI and identify concept of intelligent agent; explain various intelligent search algorithms to solve the problems; explain how to use knowledge representation in reasoning purpose; apply various techniques to an agent when acting under certainty; apply how to process natural language and other perceptual signs in order that an agent can interact intelligently with the world.

Topics:

1. Introduction to Artificial Intelligence
2. Uninformed Search Strategies
3. Introduction to Prototype Development 1 (PD1 – F2F)
4. Informed Search Strategies
5. Local Search Algorithm & Optimization Problem
6. Adversarial Search
7. Constraint Satisfaction Problems
8. VPC & Idea Profile (PD1 – Discussion Forum)
9. Logical Agent
10. First Order Logic & Inference in FOL I
11. First Order Logic & Inference in FOL II
12. Classical Palnning
13. Solution Sketch & Storyboard (PD1 – Discussion Forum)
14. Planning and Acting in the Real World
15. Knowledge Representation
16. Quantifying Uncertainty
17. Probabilistic Reasoning
18. Product / Service Prototype (PD1 – F2F)
19. Probabilistic Reasoning over TIme
20. Making Simple Decisions
21. Products / Service Features & Functions (PD1 – Discussion Forum)
22. Making Complex Decision
23. Leaning Form Examples I
24. Learning Form Examples II
25. Knowledge in Learning
26. Key Reseources & Pitch Desk (PD1 – Discussion Forum)
27. Learning Probabilistic Models
28. Reinforcement Learning
29. Natural Language Processing
30. Natural Language for Communications
31. Perception Robotics
32. Report (PD1 – F2F)