**Contents and Objectives:**-

This course gives an introduction to the philosophies and techniques of Artificial Intelligence, as well as to symbolic computing using the LISP language. AI techniques have become an essential element in modern computer software and are thus essential for a successful career and advanced studies in computer science. Students successfully completing this course will be able to apply a variety of techniques for the design of efficient algorithms for complex problems.

**Prerequisites:**-

All students are expected to have passed the courses*Programming Languages*(CSE 3302) and*Theoretical Concepts*(CSE 3315) or an equivalent before attending this course.

**Textbook:**-

S. Russell and P. Norvig, "Artificial Intelligence: A Modern Approach", second edition, Prentice Hall, 2003

**Course Materials:**-

The course will give a brief introduction to LISP and Prolog and it will be required to be able to read a limited amount of LISP code. Some students might therefore want to acquire a book teaching basic Lisp. There are several online Lisp books including Ansi Common Lisp and On Lisp (http://www.paulgraham.com/paulgraham/books.html), and Steele's Common Lisp book in HTML at the CMU AI Repository (http://www.cs.cmu.edu/Web/Groups/AI/html/cltl/cltl2.html). There are also good references for Prolog, including*Prolog Programming for Artificial Intelligence*,*The Art of Prolog*, and*Programming Prolog*.Additional course materials such as lecture notes, assignments, and solutions will be available electronically on the course web page. Changes and corrections, if any, will also be announced by e-mail.

**Computer Access:**-

This course will require some programming and all students will have an account on the ACS machines*gamma*and*omega*. If not otherwise stated on the assignment homework assignments can be programmed in the language of your choice but have to compile and run on*gamma*or*omega*. If partial code is provided, however, it will generally be only provided in a limited number of languages. Additional details will be announced in class.

**E-mail and WWW page:**-

There is a course web page at http://crystal.uta.edu/~athitsos/courses/cse4308. All changes and supplementary course materials will be available from this site. In addition, necessary changes or important announcements will also be distributed by e-mail.

**Office Hours:**-

Tuesday and Thursday, 11:00am-12 noon.TA Office hours for the course will be held by the TA, Srividhya Rajendran (e-mail: [first name] dot [last name] AT uta.edu), every Monday, Wednesday, and Friday, 10:00am - 11:00am, at NH 239.

There will be three exams in this course (two midterms and a final). The first 2 exams cover the content of the indicated chapters in the book. The final exam is cumulative and will cover all materials of the course. If for any such reason you can not attend an exam, inform the instructor as early as possible.

**
Any request for re-grading (for an assignment or exam) must be made within two weeks of receipt of that grade.
**

STUDENTS ARE FREE TO USE ANY BOOKS AND NOTES THEY WANT DURING THE EXAM.

For students enrolled in the graduate section CSE 5360 the homework assignments, as well as the exam will contain additional problems which are not required for students of CSE 4308.

Exams and homework assignments will contribute to the overall grade in the following way:

CSE 4308:

Written Assignments | 15% |

Programming Assignments | 25% |

ABET Assessment Outcome E (Identifying, Formulating, and Solving Engineering Problems) | 10% |

Midterm 1 | 15 % |

Midterm 2 | 15 % |

Final exam | 20 % |

CSE 5360:

Written Assignments | 20% |

Programming Assignments | 30% |

Midterm 1 | 15 % |

Midterm 2 | 15 % |

Final exam | 20 % |

CSE 4308 / CSE 5360 - Artificial Intelligence I | |||||

Textbook: S. Russell and P. Norwig, "Artificial Intelligence: A Modern Approach", second edition, Prentice Hall, 2003 | |||||

Tentative Lecture and Assignment Schedule | |||||

Fall Semester 2007 - TuTh 2:00 - 3:20 | |||||

Class | Date | Textbook Readings | Other Readings | Lecture Topics | Assignments |

1 | 08/28 | 1 | Slides ((c)S. Russel) | Course Details and Overview | |

2 | 08/30 | 2 | Slides ((c)S. Russel) | Introduction to AI and Agents | |

3 | 09/04 | Slides ((c) Cook) | LISP | ||

4 | 09/06 | 3 | Slides ((c)S. Russel) | Solving Problems by Search | |

5 | 09/11 | 4 | Search continued | ||

6 | 09/13 | 4 | Slides ((c)S. Russel, Cook) | Search continued | |

7 | 09/18 | 4 | Search continued | ||

8 | 09/20 | 6 | Slides ((c)S. Russel) | Game Playing | |

9 | 09/25 | 6 | Game Playing continued | ||

10 | 09/27 | 7 | Slides ((c)S. Russel) | Knowledge and Logic Reasoning | |

11 | 10/02 | 8 | Slides ((c)S. Russel) | First Order Logic, Knowledge Engineering | |

12 | 10/04 | Exam 1 | |||

13 | 10/09 | 8 | Proofs | ||

14 | 10/11 | 9 | Slides ((c)S. Russel) | Resolution - Unification | |

15 | 10/16 | 11 | Slides ((c)S. Russel) | Planning | |

16 | 10/18 | 11 | Slides part 1((c)S. Rajendran), Slides part 2((c)S. Rajendran) |
Planning continued | |

17 | 10/23 | 12 | Slides ((c)S. Russel) | Conditional Planning and Replanning | |

18 | 10/25 | 13 | Slides ((c)S. Russel) | Uncertainty | |

19 | 10/30 | 14.1-14.3 | BN Slides ((c)S. Russel) | Probabilistic Reasoning | |

20 | 11/01 | 14.7 (pages 526-527) | FLL_Fuzzy_Logic_Tutorial | Fuzzy Logic | |

21 | 11/06 | 18.1 - 18.4 | Slides ((c)S. Russel) | Learning Methods | |

22 | 11/08 | 18.1 - 18.4 | Learning Methods | ||

23 | 11/13 | 18.1 - 18.4 | Learning Methods | ||

24 | 11/15 | Exam 2 | |||

25 | 11/20 | 20 | Slides ((c)S. Russel) | Statistical Machine Learning | |

11/22 |
Thanksgiving - No Class | ||||

26 | 11/27 | 20 | Statistical Machine Learning | ||

27 | 11/29 | 20 | Slides ((c)S. Russel) | Statistical Machine Learning | |

28 | 12/04 | 20 | Statistical Machine Learning | ||

29 | 12/06 | Conclusions and Review | |||

30 | 12/11 | Final Exam, 2:00pm-4:30pm. Covers all Course Material |

This schedule is tentative and subject to change. If changes are necessary they will be announced in class and posted in the schedule on the course page.

Students can assume responsibility in two ways. First, if they choose to take the risk associated with scholastic dishonesty and any other violation of the Code of Student Conduct and Discipline, they must assume responsibility for their behaviors and accept the consequences. In an academic community, the standards for integrity are high. Second, if they are aware of scholastic dishonesty and any other conduct violations on the part of others, they have the responsibility to report it to the professor or assistant dean of students/director of student judicial affairs. The decision to do so is another moral dilemna to be faced as students define who they are. Students who violate University rules on scholastic dishonesty are subject to disciplinary penalties, including the possibility of failure in the course and dismissal from the University. Since dishonesty harms the individual, all students, and the integrity of the University, policies on scholastic dishonesty will be strictly enforced.