CS539 MACHINE LEARNING. SPRING 99
PROF. CAROLINA RUIZ
Department of Computer Science
Worcester Polytechnic Institute
Machine learning is concerned with the design and study of
computer programs that are able to improve their own performance
with experience, or in other words, computer programs that learn.
In this graduate course we cover several theoretical and practical
aspects of machine learning. We study different machine learning
techniques/paradigms, including decision trees, neural networks, genetic
algorithms, Bayesian learning, rule learning, and reinforcement learning.
We discuss applications of these techniques to problems in data
analysis, knowledge discovery and data mining.
We will closely follow the excellent recent book
by Tom M. Mitchell and will discuss several
state of the art research articles. The course will provide
substantial hands-on experience through four computer
projects. These projects use code and datasets provided online
as companions to the textbook.
For the catalog description of this course
see the WPI Graduate Catalog.
Tuesdays 5:30-8:20 pm
Students are also encouraged to attend the AIRG Seminar Thursdays at 11 am.
Prof. Carolina Ruiz
Office: FL 232
Phone Number: (508) 831-5640
Office Hours: Tu 4:30-5:20 pm, Th 10-10:50 am, or by appointment.
Other speakers may occasionally be invited to lecture to the class.
Tom M. Mitchell
- Several additional readings will be handed out during the semester.
CS 534 or equivalent, or permission of the instructor.
| Exam 1
| Exam 2
| Project 1 || 15%
| Project 2 || 15%
| Project 3 || 15%
| Project 4 || 15%
| Class Participation
|| Extra Points
Your final grade will reflect your own work and achievements
during the course. Any type of cheating will be penalized
with an F grade for the course and will be reported to the WPI Judicial Board
in accordance with the
Academic Honesty Policy.
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.
Both will be in-class exams.
There will be a total of 4 projects.
These projects may be implemented using any high level programming
language (Lisp, Prolog, C, C++, ...)
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
The code you submit for each of the projects should run on the WPI CS machines or
the CCC machines and should rely on software available on those machines only.
Construction of a decision tree learner using the
decision tree learning code from Chapter 3 of the textbook and
application of the system to several learning tasks.
Design and implementation of a learning system for face recognition
using neural networks and the error back propagation procedure.
This project is based on the
source code and dataset provided online
as a companion to Chapter 4 of the textbook.
Development of a Bayesian learning system. This system will be based on the
code from Chapter 6 of the textbook, which can be found
Given a common learning task to be determined, each student in the class is expected to:
- select an ML technique/paradigm (mutually agreed upon with the instructor)
not covered by the previous 3 projects nor by other students in the class.
These techniques include, but are not limited to,
instance-based learning, genetic algorithms, rule learning, and reinforment learning;
- research the ML literature on this technique;
- design and implement a prototype system that solves the learning task
using the chosen learning technique;
- write a webpage summarizing the relevant background knowledge and project results;
- and give a 30 minute, oral, in-class presentation describing the
achievements of this project.
A comparison of the results obtained by the different learning techniques/algorithms
will be drawn as a group effort.
Students are expected to read the material assigned for each
class in advance and to participate in class discussions.
Class participation will be taken into account when deciding
students' final grades.
CLASS MAILING LIST
The mailing list for this class is: firstname.lastname@example.org
If your email address does not belong to the class mailing list,
you can subscribe to it by sending the following
one-line email message to email@example.com:
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
(See also the list of assigned papers in the
- Tom M. Mitchell.
- P. Langley.
"Elements of Machine Learning"
Morgan Kaufmann Publishers, Inc.
- Fayyad, Piatetsky-Shapiro, Smyth, and
"Advances in Knowledge Discovery and Data Mining"
The MIT Press, 1995
for an extensive list of ML books organized by topics.
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.
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/ML RESOURCES ONLINE: