CS539 Machine Learning
Small changes to this syllabus may be made during the course of the semester.
Syllabus - Spring 2005
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 book
by Tom M. Mitchell and will discuss several
state of the art research articles. The course will provide
substantial hands-on experience through several computer
For the catalog description of this course
see the WPI Graduate Catalog.
Time: Tuesdays and Thursdays 4:00-5:20 pm
Prof. Carolina Ruiz
Office: FL 232
Phone Number: (508) 831-5640
Office Hours: Tuesdays 1:00-2:00 pm, Fridays 2:00-3:00 pm, or by appointment.
CS 534 or equivalent, or permission of the instructor.
| Projects (12% each)|| 100%
| 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 9 individual projects.
Each assignment/project will be related to the topic covered during the corresponding week.
They include implementation projects, assigned readings, and theoretical problems.
For most of the projects, we will use the
Weka is an excellent machine-leaning/data-mining environment.
It provides a large collection of Java-based mining algorithms,
data preprocessing filters, and experimentation capabilities.
Weka is open source software issued under the GNU General Public License.
For more information on the Weka system, to download the system and
to get its documentation, look at
You should download and use the latest stable GUI version of the system.
More detailed descriptions of the assignments and projects will be posted to the course webpage
at the appropriate times during the semester.
An in-class presentation of each of the assignments is be required.
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.
The mailing list for this class is: cs539-all AT cs.wpi.edu
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:
The webpages 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.
- Tom M. Mitchell.
E. Frank, I.H. Witten.
"Data Mining: Practical Machine Learning Tools and Techniques with
Morgan Kaufmann Publishers. 2000.
- 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
S. Russell, P. Norvig.
"Artificial Intelligence: A Modern Approach". Prentice Hall, Second Edition, 2002.
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.
Small changes to this syllabus may be made during the course
of the semester.