| Cary G. Gray |
|
| Office: |
Armerding 114, x5875 |
|
Office hours: |
WF 8:30–10:20 a.m. |
| |
T 3:15-4:00 p.m |
| |
MW 2:00–3:30 p.m. |
I am typically in my office much more than the posted times, and you
are welcome to stop by whenever my door is open. Check with me ahead
of time if you want to be sure that I'll be there outside scheduled
times.
Class meetings
MWF 12:45-1:50 p.m., Armerding 125
Final: 1:30–3:30 p.m., Tuesday, May 1
On-line resources
Additional (and updated) course information will be available at the
class page at
http://cs.wheaton.edu/~cgray/csci375/
I will e-mail you at your college address when there are major
updates. Be sure that you frequently read mail sent there.
Text and readings
- Russell and Norvig,
Artificial Intelligence: A Modern Approach/2e,
Prentice-Hall, 2003.
There may also be a handful of papers from the research literature,
which I will hand out or provide pointers to online.
Description
CSCI 375 Artificial Intelligence
Definition of intelligence, representation of knowledge, search
strategies, heuristics, control of process, natural language
processing, vision systems, expert systems, robotics. Integrative
issues of AI and Christianity.
Prerequisites: CSCI 357.1
Goals and objectives
The goals of this course are for you to gain:
-
an understanding of how AI fits into the development of computing;
- familiarity with the major approaches to and techniques
used in AI work;
- a grasp of the categories of problems that have shaped AI
as a pursuit; and
- insight into the assumptions and consequences of work in
AI, especially as viewed from the perspective of Christian faith.
By the end of the course, you should be able to:
-
describe and classify the techniques employed in AI systems;
- employ some of those techniques, in at least rudimentary
form;
- articulate common assumptions that underlie AI efforts; and
- evaluate the appropriateness of applications of AI and
address their ramifications.
Grading
I want your grade to reflect both your final mastery of the course
material and the degree of responsibility you have shown throughout
the semester. Your grade will be based on the following kinds of
work; the numbers at the end of each indicate the main objectives
from the list above.
Class preparation and participation
You will often have assigned reading, which you need to complete
before class.
There will sometimes be reading-preparation exercises
before class or activities (such as quizzes) in class.
(1–4)
Written exercises
There will some written assignments, most frequently based on
problems from the textbook; these assignments may require modest
programming. (Principally 1–2)
Unless otherwise specified, written work should
be neatly done on full-size paper, with multiple pages stapled
together.
Programs and projects
There will be a smaller number of more substantial programming
assignments, including one soup-to-nuts project in the latter part of
the semester. These will typically require that you submit both code
and some written description/evaluation of it. (1–2)
Classroom presentation
You will have the opportunity to present (probably in pairs) one
day's material during the period after Thanskgiving, when we are
covering application areas. (1)
Exams
Two midterm exams are planned in addition to the final, currently
scheduled for October 15 and November 7.
All exams will be cumulative. (1–4)
Essays
There will be a few essays assigned in which you will address the
philosophical and social connections of AI. (3–4)
Additional policies and notes
Attendance and participation
We will start promptly; so be considerate of the rest of us in class
by making sure you arrive on time. If you are late, please avoid
disrupting whatever is in progress when you come in. I will consider
your attendance and participation when computing your final grade;
that makes a difference primarily in borderline cases.
(On this and other matters, if you have a
legitimate reason, I'm willing to work with you, but you have to let
me know.)
Special accommodation
If you have any kind of special needs, I will work to accomodate you,
provided you let me know in a timely manner. For learning
disabilities or emergencies, it is your responsibility to let
me know of your need, but I will require confirmation with the
appropriate campus office (Registrar, Student Services, or Health
Center).
Outline
-
Introduction, history and overview (1 week)
- Classic AI (3 weeks)
-
search (including planning and games)
- deduction, logic programming, resolution
- Statistical models (3 weeks)
-
Bayesian, Markov models
- decision trees, conditional networks
- “neural” networks
- Learning (1 week)
- History and philosophy revisited (1 weeks)
- Applications, final projects (4 weeks)
A more detailed schedule will be distributed
separately, and updated online.
- 1
- This is the old prerequisite; in
the future, the prerequisites will be CSCI 335 and CSCI 345.
This document was translated from LATEX by
HEVEA.