Docstoc

Artificial Intelligence - Get as DOC

Document Sample
Artificial Intelligence - Get as DOC Powered By Docstoc
					                                            Course Outline
                            Artificial Intelligence (3 credit hours)
                                              CS560
                                              Spring 2004


Schedule      TBA                                      Website       TBA

Instructor    Syed Atif Mehdi                          Contact       Atif.mehdi@imt.edu.pk
                                                                     Phone extension: 313

Office        Second Floor                             Office        TBA
              SST Building, IMT                        Hours

Teaching      --                                       Contact       --
Assistant

Office        --                                       Office        --
                                                       Hours

Course        The course exposes the students to the current developments in the area of artificial
Description   intelligence. Students learn techniques to model human behavior. Students learn the
              methodology of development of software, emulating human behavior and skills. The course
              involves individual assignments and team-based project.

Expected      Upon completion of this course, students will:
Outcomes       Gain insight into the fields of Neural Networks and Expert Systems.
               Learn new research areas

Textbooks       Artificial Intelligence, George F. Luger
                Expert Systems, Design and Development, John Durkin

Assignments     Assignments are individual          Quizzes           Quizzes will be announced
                Academic dishonesty (cheating or                      Academic dishonesty (cheating or
                 using unfair means) in assignments                     using unfair means) in quizzes
                 will result in minimum of 0 in that                    will result in minimum of 0 in that
                 assignment and maximum „F‟ grade                       quiz and maximum „F‟ grade in
                 in the course                                          the course
                All late submissions will receive                     There is no retake of the quiz
                 zero marks.

Midterms        A single midterm exam                 Final           A single end term exam
                Course covers the first 14 lectures                   Whole course is included

Project       Team-based project

Attendance    Students missing more than 20% of the lectures will receive an “F” grade in the course.
Policy

Grading           Assignments: 10%
Policy            Quizzes: 15%
                  Midterm: 20%
                  Project: 20%
                  Final: 35%
                     Lectures, Reading Assignments, Homework Assignments Plan




Wk        1st Lecture          2nd Lecture                   Readings           Assignments


      1. Overview of IS,
 1    Back Propagation      2. Winner Takes All   Luger: Ch. 1, 14            Assignment #1
      Networks
      3. State Space
 2                          4. Expert Systems *   Luger: Ch. 2, 3 4, 7        Assignment #2
      Search, Heuristics

 3    5. Model Base ES      6. Model Base ES      Luger: Ch 7                 Assignment #3

                                                                              Assignment #4
 4    7. Case Base ES       8. Case Base ES *     Luger: Ch 7                 Project draft
                                                                              proposals due.

 5    9. Fuzzy Logic        10. Fuzzy Logic *                                 Assignment #5


 6    11. FRBS              12. FRBS *

                                                                              Project Phase I –
                                                                              Submission of
 7    13. FRBS              14. FRBS              Presentation
                                                                              finalized project
                                                                              proposal.
      15. Midterm Exam
 8    (No lecture on this   16. Version Space
      day)
                                                                              Project Phase II –
 9    17. ID 3              18. ID 3                                          Submission of
                                                                              requirements.
                                                                              Project Phase III –
 10   19. C 4.5 / C 5       20. C 4.5 / C 5                                   Submission of
                                                                              design.
      21. Hebbian           22. Hebbian
 11                                               Luger: Ch 14
      Learning              Learning *

 12   23. Memories          24. Memories          Luger: Ch 14


 13   25. LVQ               26. LVQ*              Handout

                                                                              Project Phase IV –
      27. Genetic           28. Genetic
 14                                               Handout                     Submission of final
      Algorithm             Algorithm
                                                                              product.
      29. Genetic           30. Genetic
 15
      Algorithm             Algorithm *

      Final Exam


NOTE: Assignments are to be submitted in lectures marked by an asterisk (*)

				
DOCUMENT INFO
Jun Wang Jun Wang Dr
About Some of Those documents come from internet for research purpose,if you have the copyrights of one of them,tell me by mail vixychina@gmail.com.Thank you!