Syllabus for

Artificial Intelligence

COSC 561

(MW 5:30-6:45, PH302)

Instructor: Matt Evett
Dept. Computer Science; room 512E Pray-Harrold
Tel: 734-487-1227; e-mail:; (the cosc561_Fall05 caucus).

Office Hours: MW 2:00-4:00, Tu 3:00-5:00
You may drop by at times other than office hours, but in that case I cannot guarantee that I'll be able to see you.

Prerequisite: Senior-level programming course.

Textbook: George Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, fifth edition. Addison-Wesley, 2005.

Course Summary: The course shall provide students with knowledge of the rudiments of the main subfields in the study of artificial intelligence. The course outline will roughly follow Russell & Norvig's book, but we will spend more time in some areas than does the book, and less in others.

  1. The History of AI
  2. Lisp
  3. Intelligent agents
  4. Search (graph search, heuristic search [A*], Minimax, monotonicity, acceptability)
  5. Game playing
  6. Representation via logic--first order logic, predicate logic, inferencing, backward-chaining
  7. Robotics
  8. Uncertainty & probability
  9. Learning, neural networks & evolutionary computation
  • The course is project oriented, with programming assignments spread throughout the semester.
  • Course Calendar:

    Due dates for projects are underlined . Exam dates are in bold. Be aware that due dates sometimes slip.  You are responsible for monitoring the course webcaucus for the latest deadlines.  In addition, programming assignments (particularly the later ones) may be altered to accomodate the interests of the class as the semester progresses.
    Date Text Projects due
    9/7 Ch 1  
    9/12,14 Ch 2 (Predicate Calculus)
    9/19, 21 Ch 3, 4 (Uninformed & Heuristic search) Logic 1
    9/26, 28 Hand-outs (Adversarial search--game playing)  
    10/3, 5 Ch 5 (Stochastic Methods) Search 1
    10/12, 14 Ch 6 (Production Systems) & Lisp Checkers
    10/17, 19 Ch 7 (Knowledge Representation)
    10/24, 26 Ch 8 (Strong Reasoning, Planning) Midterm Exam
    10/31, 11/2 Ch 8 & 9
    11/7, 9 Ch 9 (Reasoning amid Uncertainty, Bayesian Networks) Planning Project
    11/14, 16 Ch 9 & 10  
    11/21, Thanksgiving Ch 10 (Symbolic Machine Learning, Decision Trees)  
    11/28, 30 Ch 10 & 12 Maching Learning
    12/5, 7 Ch 12 (Evolutionary Computation)  
    12/12, 14 Ch 11 (Neural Networks)
    EC Project
    12/19, 5:30-7:00
    Final Exam (&Project?)


    Grading Policy:

    The final course grade will be a weighted average of the grades received in each of the following categories, as specified: Small projects & homeworks5%, Programs 45%, Midterm exam 20%, Final exam 30%..

    Tardiness Policy: Programming and other homework assignments will be due at the beginning of class. After that, assignments will be accepted through the start of the next scheduled class, but will suffer a full grade penalty. E.g., if a late programming assignment is worthy of an 'A', I will mark it a 'B'. Assignments more than one class late will not be accepted, and will receive a grade of 'F'.

    Attendance Policy: We're all grown-ups, when and whether you attend class is up to you. However, missed assignments and exams shall only be excused by a doctor's written note, verifying that the student was medically indisposed to attend class that day.

    Grading of Programs: Grading of programming assignments will reflect three factors, weighted as shown.

    1. (70%) Correctness -- does the program run correctly?
    2. (15%) Style -- does the code adhere to class documentation standards? Is the code indented properly? Are the variable names mneumonic? How well has the student followed the basic formatting characteristics for the language?
    3. (15%) Design -- is the program adequately decomposed (i.e., are the functions and procedures small enough to be comprehensible)? Are the class and structure definitions well chosen? How well has the student taken advantage of the language's capabilities?

    Announcements and the Web Page:

    Students should view the course web page regularly for announcements regarding programming assignments, readings, etc. I update course web pages frequently!

    Academic Irregularities:

    Students are required to attend to the policy on academic irregularity outlined in the EMU student handbook (see the full policy at In addition, collaboration among students in solving programming and homework assignments is forbidden. If I receive programs or homework assignments that are substantially equivalent, or which are not the original work of the student submitting the material, I will not hesitate to punish all involved parties to the fullest extent, up to and including assignment of a failing grade for the course, and referral to the Office of Judicial Student Services for possible punitive action at the University level, which may include expulsion from the University. In addition, the University and the computer science department maintain policies regarding proper behavior on its computer systems. Failure to adhere to these policies can result in loss of computer privileges, and possible legal action.