COURSE DESCRIPTION:
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
"Machine Learning"
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
projects.
For the catalog description of this course
see the WPI Graduate Catalog.
CLASS MEETING:
Time: Tuesdays and Thursdays 4:00-5:20 pm
Room: SH202
Prof. Carolina Ruiz
Office: FL 232
Phone Number: (508) 831-5640
Office Hours: Tuesdays 1:00-2:00 pm, Thursdays 2:00-3:00 pm, or by appointment.
CS 534 or equivalent, or permission of the instructor.
| Project 1: | 4%
|
| Projects 2-9 (12% each): | 96%
|
| 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.
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.
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 system
(http://www.cs.waikato.ac.nz/ml/weka/).
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
Weka's webpage
(http://www.cs.waikato.ac.nz/ml/weka/).
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 will be required.
The mailing list for this class is:
This mailing list reaches the professor and all the students in the class.
The webpages for this class are located at
http://www.cs.wpi.edu/~cs539/s07/
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.
Small changes to this syllabus may be made during the course
of the semester.
- Tom M. Mitchell.
"Machine Learning"
McGraw-Hill, 1997.
-
E. Frank, I.H. Witten.
"Data Mining: Practical Machine Learning Tools and Techniques with
Java Implementations".
Morgan Kaufmann Publishers. 2000.
- P. Langley.
"Elements of Machine Learning"
Morgan Kaufmann Publishers, Inc.
1996.
- Fayyad, Piatetsky-Shapiro, Smyth, and
Uthurusamy, eds.
"Advances in Knowledge Discovery and Data Mining"
The MIT Press, 1995
General AI
-
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
Addison Wesley.
-
S. L. Tanimoto.
"The Elements of Artificial Intelligence
Using Common Lisp"
Computer Science Press
1990.
-
E. Rich and K. Knight.
"Artificial Intelligence" Second edition
McGraw Hill
1991.
-
P. Norvig.
"Paradigms of Artificial Intelligence Programming:
Case Studies in Common Lisp"
Morgan Kaufmann Publishers, 1992.
-
M. Ginsberg.
"Essentials of Artificial Intelligence"
Morgan Kaufmann Publishers, 1993.
-
G. F. Luger and W. A. Stubblefield.
"Artificial Intelligence
Structures and Strategies for Complex Problem Solving"
Third edition
Addison-Wesley, 1998.
-
M.R. Genesereth and N. Nilsson.
"Logical Foundations of Artificial Intelligence"
Morgan Kaufmann, 1987.