This is an introductory graduate AI course.
During the first part of the semester we will cover general
knowledge representation techniques and problem solving strategies.
Topics will include
reasoning with uncertainty,
and probabilistic reasoning.
During the second part of the semester we will discuss three important
application areas in AI: machine learning, natural
language processing, and machine vision.
For the catalog description of this course
see the WPI Graduate Catalog.
Mondays 6:00 - 8:50 p.m.
Students are also encouraged to attend the
Thursdays at 11 am and the
Fridays at 10 am.
Prof. Carolina Ruiz
ruiz -A-T- cs.wpi.edu
Office: FL 232
Phone Number: (508) 831-5640
Office Hours: Thursdays 2-3 pm, or by appointment.
Familiarity with data structures and a recursive high-level language.
| Exam 1 || 25%
| Exam 2 || 25%
| Project || 25%
| Homework || 25%
| Class Participation || Extra Points
Your final grade will reflect your own work and achievements
during the course. Any type of cheating will be
penalized in accordance to the
Academic Honesty Policy.
Students are expected to read the material assigned to each
class in advance and to participate in class.
Class participation will be taken into account when deciding
students' final grades.
There will be a total of 2 exams. Each exam will cover the
material presented in class since the beginning of the semester.
In particular, the final exam is cumulative.
The midterm exam is scheduled for October 17th and
the final exam is scheduled for December 12th.
Both will be in-class exams.
There will be a total of 2 projects.
These projects may be implemented using any high level programming
More detailed descriptions of the projects will be posted to the course webpage
at the appropriate times during the semester.
Although you may find similar programs/systems available online or in the references,
the design and all code you use and submit for your projects MUST be your own original work.
CLASS MAILING LIST
The mailing list for this class is: cs534-all AT cs.wpi.edu
This mailing list reaches the professor and all the students in the class.
CLASS WEB PAGES
The web pages for this class are located at
Announcements will be posted on the web pages and/or
the class mailing list, and so you are urged to check your email and
the class web pages frequently.
ADDITIONAL SUGGESTED REFERENCES
The following additional references complement and/or supplement
the material contained in the required textbook. I have listed
them in decreasing order of interest according to my preferences.
T. Dean, J. Allen, Y. Aloimonos.
"Artificial Intelligence: Theory and Practice"
The Benjamin/Cummings Publishing Company, Inc. 1995.
B. L. Webber, N. J. Nilsson, eds.
"Readings in Artificial Intelligence"
Tioga Publishing Company, 1981.
Patrick H. Winston.
"Artificial Intelligence" 3rd edition
S. L. Tanimoto.
"The Elements of Artificial Intelligence
Using Common Lisp"
Computer Science Press
E. Rich and K. Knight.
"Artificial Intelligence" Second edition
"Paradigms of Artificial Intelligence Programming:
Case Studies in Common Lisp"
Morgan Kaufmann Publishers, 1992.
"Essentials of Artificial Intelligence"
Morgan Kaufmann Publishers, 1993.
G. F. Luger and W. A. Stubblefield
Structures and Strategies for Complex Problem Solving"
M.R. Genesereth and N. Nilsson,
"Logical Foundations of Artificial Intelligence"
Morgan Kaufmann, 1987.
- Tom M. Mitchell
- P. Langley
"Elements of Machine Learning"
Morgan Kauffamann Publishers, Inc.
Lisp/Prolog Textbooks and Manuals
G. L. Steele Jr.
"Common Lisp: The language'' 2nd edition
Digital Press, 1990.
This reference is online.
Patrick H. Winston and Berthold K.P. Horn "Lisp" 3rd edition.
L. Sterling, E. Shapiro "The Art of Prolog" MIT Press, 1986.
Small changes to this syllabus may be made during the course
of the term.
OTHER AI RESOURCES ONLINE: