CS 344 - Artificial Intelligence/ CS 386 - Artificial Intelligence Lab.

Spring 2011

Instructor: Prof. Pushpak Bhattacharyya (pb[AT]cse.iitb.ac.in)

TAs:

• Joydip(joydip[AT]cse.iitb.ac.in)

• Ganesh(ganeshbhosale[AT]cse.iitb.ac.in)

• Janardhan(janardhan[AT]cse.iitb.ac.in)

• Srijit(srijitdutt[AT]cse.iitb.ac.in)

Lecture Schedule and Venue

Timings: Monday 9:30AM-10:30AM, Tuesday 10:30AM-11:30PM, Thursday 11:30AM-12:30AM

Venue: SIC 301, Kanwal Rekhi Building

Student seminars

Groupwise list of seminar topics

Lecture Notes

1. Lecture 1, Jan 3: Introduction[PDF][PPTX]

2. Lecture 2, Jan 4: Introduction: Astar search Algorithm[PDF][PPTX]

3. Lecture 3, Jan 6: Astar search Algorithm properties[PDF][PPTX]

4. Lecture 4, Jan 11: A* and its properties cntd; monotonicity[PDF][PPTX]

5. Lecture 5, Jan 13: Monotonicity[PDF][PPTX]

6. Lecture 6 and 7, Jan 18: HMM[PDF][PPTX]

7. Lecture 8, Jan 24: HMM - Viterbi[PDF][PPTX]

8. And-Or-graph[PDF][PPTX]

9. Quiz-1 : A* Search Algorithm[PDF][PPTX]

10. Lecture 9, Jan 25: Viterbi, Forward-Backward [PDF][PPTX]

11. Lecture 10, 11, Jan 27, 31: Forward backward, HMM Training[PDF][PPTX]

12. Lecture 12, 13, Feb 01, 02: POS Tagging Baum Welch[PDF][PPTX]

13. Lecture 14, Feb 07, 02: AI Puzzle Solving[PDF][PPTX]

14. Lecture 15, 16, Feb 08, 14: Predicate Calculus[PDF][PPTX]

15. Lecture 17,18, Feb 15, 16: Predicate Calculus, Inference and Interpretation[PDF][PPTX]

16. Lecture 19 Feb 17: Predicate Calculus, Inference and Interpretation[PDF][PPTX]

17. Lecture 20 Feb 28: Neural Network[PDF][PPTX]

18. Lecture 23 Mar 8: Neural Network, perceptron computing power[PDF][PPTX]

19. Extra Lecture Mar 3: Text Entailment[PPTX]

20. Lecture 24 Mar 10: Perceptron capacity generating function[PDF][PPTX]

21. Lecture 25 Mar 14: No of regions perceptron training[PDF][PPTX]

22. Lecture 26 Mar 15: Prolog[PDF][PPTX]

23. Lecture 27-28 Mar 17: Prolog Hialayan club[PDF][PPTX]

24. Lecture 29 Mar 22: perceptron-training-convergence[PDF][PPTX]

25. Lecture 30 Mar 24: pta-convergence[PDF][PPTX]

26. Lecture 31 Mar 28: Sigmoid Function[PDF][PPTX]

27. Lecture 32 Mar 29: sigmoid-backprop[PDF][PPTX]

28. Lecture 33 Mar 31: sigmoid-backprop[PDF][PPTX]

29. Lecture 34 Apr 5: bp issues[PDF][PPTX]

30. Lecture 35 Apr 7: bp issues application[PDF][PPTX]

31. Lecture 36-37 Apr 11-12: bp issues hardness[PDF][PPTX]

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Lab Assignments
Group Details: Here

Assignment 1: Implementation of the A-star algorithm
Assignment 2a: Introduction to HMM
Assignment 2b: Using HMM for Real World Applications

Resources

• Link to Last year webpage

• Link to 2009 webpage

• Link to 2008 webpage

• Suggested Reading Material:

  1. Elaine Rich & Kevin Knight, "Artificial Intelligence", McGraw-Hill Science/Engineering/Math; 2nd edition.

  2. Russel S. and Norvig P., "Artificial Intelligence: a Modern Approach", Prentice Hall, 1998.

  3. Nilsson, N.J., "Artificial Intelligence, a New Approach", Morgan Kaufmann, 2000.

  4. Mitchell, T., "Machine Learning", McGraw-Hill, 1997.

• Papers: Selected, topic based papers from the

  1. Journals: Artificial Intelligence, Artificial Intelligence Programming, Machine Learning, IEEE Expert, Data and Knowledge Engineering, Pattern Recognition etc.

  2. Conferences: AAAI, IJCAI, UAI, ICML, ACL etc.

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