WPI Worcester Polytechnic Institute

Computer Science Department
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CS539 Machine Learning 
Syllabus - Fall 2000

PROF. CAROLINA RUIZ 

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

CLASS MEETING:

Thursdays 6:00-8:50 pm
FL311

Students are also encouraged to attend the KDDRG Seminar Fridays at 2:00 pm.

INSTRUCTOR:

Prof. Carolina Ruiz
ruiz@cs.wpi.edu
Office: FL 232
Phone Number: (508) 831-5640
Office Hours: Mo 11:00-12:00 m, Th 3:00-4:00 pm, or by appointment.

Other speakers may occasionally be invited to lecture to the class.

TEXTBOOK:

PREREQUISITE:

CS 534 or equivalent, or permission of the instructor.

GRADES:

Exam 20%
Weekly Assignments 80% (8% each)
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.

EXAMS

There will be a final exam. The exam will cover the material covered in class since the beginning of the semester.

PROJECTS AND ASSIGNMENTS

There will be a total of 10 projects. Each assignment/project will be related to the topic covered during the corresponding week. They include implementation projects, assigned readings, theoretical problems, and individual project assignments.

Projects that involved developing code may be implemented using any high level programming language (Lisp, Prolog, C, C++, ...) 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.

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.

CLASS PARTICIPATION

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: cs539@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 majordomo@cs.wpi.edu: subscribe cs539

CLASS WEB PAGES

The web pages for this class are located at http://www.cs.wpi.edu/~cs539/f00/
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

Machine Learning

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

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

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

  4. See http://www.aic.nrl.navy.mil/~aha/research/ml/books.html for an extensive list of ML books organized by topics.

General AI

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

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

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

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

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

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

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

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

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

Lisp/Prolog Textbooks and Manuals

  1. G. L. Steele Jr. "Common Lisp: The language'' 2nd edition Digital Press, 1990. (ISBN 1-55558-041-6)
    This reference is online.

  2. Patrick H. Winston and Berthold K.P. Horn. "Lisp" 3rd edition.

  3. L. Sterling, E. Shapiro. "The Art of Prolog" MIT Press, 1986.

WARNING:

Small changes to this syllabus may be made during the course of the semester.

OTHER AI/ML RESOURCES ONLINE: