CSC 550: Introduction to Artificial Intelligence

Fall 2004


5:30-7:20 M, 9:30-10:20 W
411 Old Gymnasium
Dr. David Reed
207 Old Gymnasium      x2583
DaveReed@creighton.edu



Text: Artificial Intelligence: Structures and Strategies for Complex Problem Solving (4th ed.),
George F. Luger, Addison-Wesley, 2002.


Course Description

Artificial Intelligence is the subfield of computer science concerned with automating tasks that would require "intelligence" if performed by people. AI is a highly eclectic field, with roots in mathematics, logic, psychology, philosophy, and engineering. The goal of this course is to introduce and survey the field of Artificial Intelligence, paying special attention to foundational concepts and theories. In addition, current trends and approaches in AI research will be studied.

Specific goals for the course are:


Required Work

There will be five to seven homework assignments spread throughout the term. These assignments will cover concepts and problems from class and the readings, and may involve writing and modifying AI programs in Scheme. Assignments are due at the beginning of class on the date specified. Late assignments will receive 75% of full credit if they are handed in within one week of the specified due date. After one week, no credit will be given. In addition, students will be expected to independently research a topic in AI and present that topic to the class. There will be weekly quizzes, a midterm exam and a cumulative final exam (see the schedule below for exam dates).

There is no specific attendance policy for the course, although it is expected that absences will leave the student unprepared for tests and assignments. Quizzes and tests will not be rescheduled except in extreme circumstances. However, the lowest quiz grade will be dropped.

Grades will be determined as follows:

homework assignments 35 %
student presentation 10 %
weekly quizzes 05 %
midterm exam 20 %
(cumulative) final exam 30 %

At the minimum, traditional grading cutoffs will apply. That is, 90% is guaranteed an A, 87% is guaranteed a B+, etc. Depending on class performance, some shifting of grades (in an upward direction only) may occur as final letter grades are assigned.


Policy on Collaboration

The college policy on cheating and plagiarism is spelled out in the Student Handbook. In addition to this, the following guidelines hold pertaining to programs. Programs are to be the sole work of the student -- collaboration on the design or coding of a program is not allowed. Questions regarding homework assignments should be directed at the instructor only. Students may seek debugging assistance or clarifications on assignments using the class Discussion Board. Repeat: All student interactions regarding homework assignments must take place via the class Discussion Board!

Violations of this policy will be dealt with severely, with possible outcomes including failure in the course and expulsion from the university. In the case of programming assignments, you are encouraged to start early so that there is time to seek help from the instructor as the need arises.


Tentative Schedule

DATES
TOPICS
READINGS
HOMEWORK
Aug 30
 
Course overview, AI history.
 
(ppt)
(pdf)
Chapter 1 HW1: due 9/13
Sep 6
8
NO CLASS -- LABOR DAY
AI PROGRAMMING:
(ppt)
(pdf)
Chapter 15, Online
13
15
    Scheme, atoms, lists,
    functions, if/cond,
20
22
    recursion, data structures,
    AI applications.
Chapter 2 HW2: due 10/4
27
29
AI AS SEARCH:
    state spaces, uninformed strategies.
(ppt)
(pdf)
Chapter 3
Oct 4
6
Heuristics,
    informed strategies.
(ppt)
(pdf)
Chapter 4 HW3: due 10/15
11
13
MIDTERM EXAM
Search applications,
(ppt)
(pdf)
Chapter 5 Presentation
18
20
NO CLASS -- FALL RECESS
25
27
     Algorithm A,
     game trees.
Chapter 5
Nov 1
3
REPRESENTATION & AI:
    semantic nets, frames, scripts.
(ppt)
(pdf)
Chapter 6 HW4: due 11/12
8
10
Expert systems, uncertainty,
    user interface.
(ppt)
(pdf)
Chapters 7, 8
15
17
MACHINE LEARNING:
    connectionist models, neural nets,
(ppt)
(pdf)
Chapters 9, 10
22
24
    associative memory.
NO CLASS -- THANKSGIVING
HW5: due 12/6
29
Dec 1
Emergent models, genetic algorithm,
    cellular automata.
(ppt)
(pdf)
Chapter 11
6
8
    alife.
Presentations (planning, data mining).
Dec 13 FINAL EXAM    (Mon 5:30 - 7:20)