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

Spring 2010

Student Seminars Lecture Notes Resources Marks/Grades Lab Assignments Student Groups/Teams

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

TAs:

  • Prashanth(pkamle[AT]cse.iitb.ac.in)
  • Debraj(debraj[AT]cse.iitb.ac.in)
  • Ashutosh(ashu[AT]cse.iitb.ac.in)
  • Nirdesh(nirdesh[AT]cse.iitb.ac.in)
  • Raunak(rpilani[AT]cse.iitb.ac.in)
  • Gourab(roygourab[AT]cse.iitb.ac.in)

    • Assignment marks announced!!! Available here

    Lecture Schedule and Venue

    Timings: Monday 10:30AM-11:30AM, Tuesday 11:30AM-12:30PM, Thursday 8:30AM-9:30AM

    Venue: SIC 301, Kanwal Rekhi Building

    Student seminars

    Groupwise list of seminar topics

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    Lecture Notes
    1. Lecture 1, Jan 4: Introduction[PDF][PPTX]
    2. Lecture 2, Jan 5: Fuzzy logic and Inferencing[PDF][PPTX]
    3. Lecture 3, Jan 7: Fuzzy Inferencing: Inverted pendulum[PDF][PPTX]
    4. Lecture 4, Jan 11: Fuzzy control of Inverted pendulum and propositional calculus based puzzles[PDF][PPTX]
    5. Lecture 5 and 6, Jan 14, 18: Propositional calculus and co-operative puzzle solving[PDF][PPTX]
    6. Lecture 7, Jan 19: Predicate calculus[PDF][PPTX]
    7. Lecture 8 and 9, Jan 24: Predicate calculus - Interpretation and Inferencing[PDF][PPTX]
    8. Lecture 10, Jan 26: Club and circuit examples[PDF][PPTX]
    9. Lecture 11, Jan 27: Prolog[PDF][PPTX]
    10. Lecture 12, Jan 28: Prolog examples[PDF][PPTX]
    11. Lecture 13, Feb 8: Search[PDF][PPTX]
    12. Lectures 14,15,16; Feb 9 to Feb 11: Search: Algorithmics, admissibility[PDF][PPTX]
    13. Lecture 17, Feb 22: Theorems in A* search[PDF][PPTX]
    14. Lectures 18,19; Feb 23, 24: Natural language processing, ambiguities and machine learning[PDF][PPTX]
    15. Lectures 20,21; Feb 24,25: Natural language parsing[PDF][PPTX]
    16. Lectures 22,23; Mar 3: Herbrand's theorem[PDF][PPT]
    17. Lectures 24,25; Mar 8,9: Argmax computation[PDF][PPTX]
    18. Lectures 26,27; Mar 11,15: Probabilistic parsing[PDF][PPTX]
    19. Lecture 30; Mar 22: Probabilistic parsing algorithms[PDF][PPTX]
    20. Lecture 31; Mar 23: Information retrieval[PDF][PPTX]
    21. Lectures 32, 33; Mar 25,29: Information retrieval: Basic concepts and model[PDF][PPTX]
    22. Lectures 34, 35; Mar 30, Apr 1: CLIR ranking[PDF][PPTX]
    23. Lectures 36, 37; Apr 2: Foundations of Machine learning[PDF][PPTX]
    24. Lectures 38; Apr 5: PAC Learning, VC dimensions; Self Organization[PDF][PPTX]
    25. Lectures 39; Apr 6: Recap[PDF][PPTX]
    26. Lectures 40; Apr 8: Text Entailment[PPT]

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    Lab Assignments

    Resources top