People Innovation Excellence


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 various AI algorithms to solve the problems.


  1. Introduction to Artificial Intelligence
  2. Search Strategies
  3. Introduction to Prototype Development 1 (PD1 – F2F)
  4. Local Search
  5. Adversarial Search
  6. Logical Agents
  7. First Order Logic
  8. VPC & Idea Profile (PD1 – Discussion Forum)
  9. Fuzzy Systems
  10. Quantifying Uncertainty 1
  11. Quantifying Uncertainty 2
  12. Probabilistic Reasoning
  13. Solution Sketch & Storyboard (PD1 – Discussion Forum)
  14. AI Application
  15. Probabilistic Reasoning Over Time
  16. Project Proposal Presentation
  17. Product / Service Prototype (PD1 – F2F)
  18. Learning from Examples
  19. Introduction to Machine Learning
  20. Products / Services Features & Function (PD1 – Discussion Forum)
  21. Linear Regression & Classification
  22. K-Nearest Neighbour
  23. Support Vector Machine
  24. Artificial Neural Network
  25. Key Resources & Pitch Deck
  26. Learning Probabilistic Models
  27. Natural Language Processing
  28. Natural Language for Communication
  29. Introduction to Computer Vision
  30. Project Presentation
  31. Report (PD1 – F2F)

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