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.
Tuesdays and Thursdays 4:00 - 5:20 p.m.
Students are also encouraged to attend the
Thursdays at 11 am and the
Fridays at 2 pm.
Prof. Carolina Ruiz
Office: FL 232
Office Hours: Mondays 1-2 pm, Thursdays 2-3 pm, or by appointment.
Familiarity with data structures and a recursive high-level language.
| Exam 1 || 20%
| Exam 2 || 20%
| Project || 25%
| Homework || 35%
| 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 10 and
the final exam is scheduled for December 12.
HOMEWORK AND PROJECS
There will be several, individual homework assignments during the semester.
The homework statements will be posted on the course webpage.
Generally homework solutions are due on Tuesdays.
Each student should hand-in his/her own individual written homework solutions
at the beginning of the class when the homework is due, and
should be prepared to present and discuss
his/her homework solutions in class immediatly after.
There will be one major course project. This project may consist of several
A detailed description of the project will be posted to the course webpage
at the appropriate time during the semester.
Although you may find similar programs/systems available online or in the
the design and all code you use and submit for you projects MUST be your own original work.
CLASS MAILING LIST
The mailing list for this class is:
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 own 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:
- Previous course offerings
Webpages of my previous offerings of this and other related courses have some
useful material: practice exams, exams, homework, solutions to exams/hw, etc.
- Artificial Intelligence. CS534
- Machine Learning. CS539
- Knowledge Discovery and Data Mining. CS525 KDD
- Introduction to Artificial Intelligence. CS4341
- Data Mining and Knowledge Discovery in Databases. CS444X and CS4445