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CS 305 Introduction to Artificial Intelligence
Walker, Regina Gail


Mission Statement: The mission of Park University, an entrepreneurial institution of learning, is to provide access to academic excellence, which will prepare learners to think critically, communicate effectively and engage in lifelong learning while serving a global community.

Vision Statement: Park University will be a renowned international leader in providing innovative educational opportunities for learners within the global society.
CourseCS 305 Introduction to Artificial Intelligence DL
SemesterF1T2005
FacultyWalker, Regina G.
TitleComputer Science Instructor/Adjunct Faculty
Degrees/CertificatesB.S. Computer Science, The University of Texas at San Antonio
M.S. Computer Information Systems, St. Mary's University
E-Mailregina.walker@park.edu
Semester DatesAugust 29 - October 16, 2005
Credit Hours3

Textbook:
Russell and Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall (Second Edition), 2003.

Textbooks can be purchased though the MBS bookstore

Textbooks can be purchased though the Parkville Bookstore


Course Description:
The student will learn the terminology and methods used in a variety of  artificial-intelligence (AI) areas. These topics will be covered: history of  artificial intelligence, search techniques, knowledge representation. In  addition, one or more of these topics will be covered: expert systems,  uncertainty, case-based reasoning, neural networks, vision, robotics. The  student may use various AI tools, Lisp, and/or Prolog for AI projects.  Prerequisite: CS352. 3:0:3

Educational Philosophy:
The instructor's educational philosophy is one of interactiveness based on lectures, readings, dialogues with other students, examinations, the Internet, web sites and writings or programming assignments.

Learning Outcomes:

After completing this course, the student should be able to:

-  Explain the history of artificial intelligence and understand the various definitions;
-  Explain and trace various search algorithms;
-  Solve first-order logic problems;
-  Explain knowledge representation techniques;
-  Do one or two of these:
  -- Solve problems involving uncertainty;
  -- Explain and trace machine learning techniques, including neural network learning;
  -- Explain and analyze vision concepts;
  -- Explain and analyze robotics concepts;
-  Write programs written in Lisp, Prolog, Java, C++, or another language that implement artificial intelligence algorithms for some of the object items

Course Assessment:
Course assessment will be accomplished through homework assignments, a class project (paper or AI program), and discussion assignemnts.

Grading:
Course assignments are as follows:
-  Six homework assignments, 50 points each, for a total 300 points
-  One project for a total of 250 points
-  One final exam for a total of 300 points
-  Eight weekly discussion assignments (Weeks 1 and 8 are worth 10 points each, Weeks 2 through 7 are worth 20 points each, instructor will provide a comprehensive grade at the end of the term worth 10 points) for a total of 150 points

Late Submission of Course Materials:
Late work will be accepted but up to 5% of the total grade may be deducted for each late any assignment is turned in late.

Classroom Rules of Conduct:
Each student is responsible for:

-- Completing all reading assignments
-- Completing all Homework the date provided by the instructor
-- Participating in Discussions.
-- Completing the Class Project (a program or research paper) by Wednesday, Week 8
-- Completing the Final Examination

Course Topic/Dates/Assignments:
Topics for each week are outlined below:
- Week 1: Introduction
- Week 2: Problem Solving and Searching
- Week 3: Knowledge and Reasoning
- Week 4: Uncertain Knowledge and Reasoning
- Week 5: The Learning Agent
- Week 6: Perceiving and Acting
- Week 7: Conclusions
- Week 8: Final Exam

 Class ActivitiesAssignmentsTests
Week 1- Complete the following readings:
-- Course Text:
Chapter 1: All sections
Chapter 2: All sections
-- Online Articles:
What is Artificial Intelligence by John McCarthy
FAQ 1-10: What are good programming languages for AI? From the  Artificial Intelligence FAQ's collection that moved from CMU to UCLA and is maintained by Amit Dubey and Ric Crabbe; written by Ric Crabbe, Amit Dubey, and Mark Kantrowitz
- View the Lecture
- View the Lisp Tutorial
-  Respond to the Individual Dicussion for the week
-  Complete the Homework for the week
Complete the Self-Check
Week 2- Complete the following readings in the Course Text:
-- Chapter 3: Sections 3.1 through 3.5, and 3.7
-- Chapter 4: Sections 4.1, 4.2, 4.5, and 4.6
-- Chapter 6: Skim entire chapter.
- View the Lecture
-  Respond to the Group Discussion for the week
-  Complete the Homework for the week
Complete the Self-Check
Week 3- Complete the following readings in the Course Text:
-- Chapter 7: Study sections 7.1 through 7.5, and 7.8.
-- Chapter 8: Study all sections.
-  View the Lecture
- View the First-Order Logic Tutorial
-  Respond to the Group Discussion for the week
-  Complete the Homework for the week
Complete the Self-Check
Week 4- Complete the following readings in the Course Text:
-- Chapter 13: Study sections 13.1 through 13.4, 13.6, and 13.8
-- Chapter 14: Study sections 14.1, 14.2, 14.7, and 14.8
- View the Lecture
-  Respond to the Group Discussion for the week
-  Complete the Homework for the week
Complete the Self-Check
Week 5- Complete the following readings in the Course Text:
-- Chapter 18: Study sections 18.1 through 18.3 and 18.6
-- Chapter 20: Study sections 20.5 and 20.8
-  Respond to the Group Discussion for the week
-  Complete the Homework for the week
Complete the Self-Check
Week 6- Complete the following readings in the Course Text:
-- Chapter 24: Study all sections
-- Chapter 25: Study sections 25.1 through 25.5, 25.7, and 25.8
- View the Lecture
-  Respond to the Group Discussion for the week
-  Complete the Homework for the week
Complete the Self-Check
Week 7- Complete the following readings in the Course Text:
-- Chapter 26: Study all sections
- Respond to two Individual Discussion assignments for the week
 
Week 8Take Proctored Final Exam- Respond to the Individual Discussion assignment for the week
- Submit Class Project by Wednesday to the Dropbox for Week 8: Project Box
Final Exam

Academic Honesty:
Academic integrity is the foundation of the academic community. Because each student has the primary responsibility for being academically honest, students are advised to read and understand all sections of this policy relating to standards of conduct and academic life.   Park University 2005-2006 Undergraduate Catalog Page 85-87

Plagiarism:
Plagiarism involves the use of quotations without quotation marks, the use of quotations without indication of the source, the use of another's idea without acknowledging the source, the submission of a paper, laboratory report, project, or class assignment (any portion of such) prepared by another person, or incorrect paraphrasing. Park University 2005-2006 Undergraduate Catalog Page 85-87

Attendance Policy:
Instructors are required to maintain attendance records and to report absences via the online attendance reporting system.

  1. The instructor may excuse absences for valid reasons, but missed work must be made up within the semester/term of enrollment.
  2. Work missed through unexcused absences must also be made up within the semester/term of enrollment, but unexcused absences may carry further penalties.
  3. In the event of two consecutive weeks of unexcused absences in a semester/term of enrollment, the student will be administratively withdrawn, resulting in a grade of "WH".
  4. A "Contract for Incomplete" will not be issued to a student who has unexcused or excessive absences recorded for a course.
  5. Students receiving Military Tuition Assistance or Veterans Administration educational benefits must not exceed three unexcused absences in the semester/term of enrollment. Excessive absences will be reported to the appropriate agency and may result in a monetary penalty to the student.
  6. Report of a "F" grade (attendance or academic) resulting from excessive absence for those students who are receiving financial assistance from agencies not mentioned in item 5 above will be reported to the appropriate agency.
ONLINE NOTE: An attendance report of "P" (present) will be recorded for students who have logged in to the Online classroom at least once during each week of the term. Recording of attendance is not equivalent to participation. Participation grades will be assigned by each instructor according to the criteria in the Grading Policy section of the syllabus.

Park University 2005-2006 Undergraduate Catalog Page 89

Park University 2005-2006 Undergraduate Catalog Page 89

Disability Guidelines:

Park University is committed to meeting the needs of all students that meet the criteria for special assistance. These guidelines are designed to supply directions to students concerning the information necessary to accomplish this goal. It is Park University's policy to comply fully with federal and state law, including Section 504 of the Rehabilitation Act of 1973 and the Americans with Disabilities Act of 1990, regarding students with disabilities. In the case of any inconsistency between these guidelines and federal and/or state law, the provisions of the law will apply. Park University is committed to meeting the needs of all learners that meet the criteria for special assistance. These guidelines are designed to supply directions to learners concerning the information necessary to accomplish this goal. It is Park University's policy to comply fully with federal and state law, including Section 504 of the Rehabilitation Act of 1973 and the American with Disabilities Act of 1990, regarding learners with disabilities and, to the extent of any inconsistency between these guidelines and federal and/or state law, the provisions of the law will apply. Additional information concerning Park University's policies and procedures related to disability can be found on the Park University web page:
http://www.park.edu/disability
 
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Copyright:
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