CS539 Machine Learning

Syllabus— Spring 2007

Prof. Carolina Ruiz

WARNING: Small changes to this syllabus may be made during the semester.


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.


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.


Machine Learning

  1. Tom M. Mitchell. "Machine Learning" McGraw-Hill, 1997.

  2. E. Frank, I.H. Witten. "Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations". Morgan Kaufmann Publishers. 2000.

  3. P. Langley. "Elements of Machine Learning" Morgan Kaufmann Publishers, Inc. 1996.

  4. Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy, eds. "Advances in Knowledge Discovery and Data Mining" The MIT Press, 1995

General AI

  1. S. Russell, P. Norvig. "Artificial Intelligence: A Modern Approach". Prentice Hall, Second Edition, 2002.

  2. T. Dean, J. Allen, Y. Aloimonos. "Artificial Intelligence: Theory and Practice" The Benjamin/Cummings Publishing Company, Inc. 1995.

  3. B. L. Webber, N. J. Nilsson, eds. "Readings in Artificial Intelligence" Tioga Publishing Company, 1981.

  4. Patrick H. Winston. "Artificial Intelligence" 3rd edition Addison Wesley.

  5. S. L. Tanimoto. "The Elements of Artificial Intelligence Using Common Lisp" Computer Science Press 1990.

  6. E. Rich and K. Knight. "Artificial Intelligence" Second edition McGraw Hill 1991.

  7. P. Norvig. "Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp" Morgan Kaufmann Publishers, 1992.

  8. M. Ginsberg. "Essentials of Artificial Intelligence" Morgan Kaufmann Publishers, 1993.

  9. G. F. Luger and W. A. Stubblefield. "Artificial Intelligence Structures and Strategies for Complex Problem Solving" Third edition Addison-Wesley, 1998.

  10. M.R. Genesereth and N. Nilsson. "Logical Foundations of Artificial Intelligence" Morgan Kaufmann, 1987.


WPI Worcester Polytechnic Institute

Computer Science Department