George Mason University
The Volgenau School of Information Technology and Engineering
Department of Computer Science

CS 782 Machine Learning


Meeting time: Thursday 7:20 pm – 10:00 pm

Meeting location: Art and Design Building 2026


Instructor: Dr. Gheorghe Tecuci, Professor of Computer Science

Office hours: Thursday 6:10 pm – 7:10 pm
Office: Nguyen Engineering Building 4613
Phone: 703 993 1722
E-mail: tecuci at gmu dot edu


Course Description


Machine Learning is concerned with developing intelligent adaptive systems that are able to improve their competence and/or efficiency through learning from input data, from a human user, or from their own problem solving experience. This course presents the principles, strategies, major methods, systems, open issues and research directions in Machine Learning, preparing the students to build evolving knowledge-based systems. Covered topics include: concept learning, decision-tree learning, Bayesian learning, support vector machines, instance-based learning, case-based reasoning, learning by analogy, inductive logic programming, explanation-based learning, abduction and discovery. The relative strengths and weaknesses of these strategies, as well as their most appropriate application domains will be discussed. The course will also cover multistrategy learning which is concerned with building advanced learning systems that integrate several basic learning strategies in a synergistic way, in order to solve learning problems that are beyond the capabilities of the integrated strategies. It will also discuss automated knowledge acquisition, integrated teaching and learning, and instructable agents, as well as open issues, current trends and frontier research in Machine Learning. Interested students may work on projects relevant to their research area or may experiment with or enhance the Disciple learning and reasoning agent developed in the Learning Agents Center (


This course will use Blackboard (see to post the lecture notes and the grades. Students have accounts on Blackboard and can download the PDF slide files by going to and logging in using their Mason ID and passwords.


Grading Policy


There will be a mid-term exam and a final exam. It will be permissible, on an individual basis, to replace the final exam with a project. This option requires an early submission of a proposal.


The course grade will be determined as follows:

Class participation 20%

Mid-term exam 40%

Final exam or project 40%


Exam Dates


Mid-term exam: 03/25/2010

Final exam: 05/6/2010


Required Readings


Tecuci G., Lecture Notes in Machine Learning, 2010 (available online).


Mitchell T., Machine Learning, New York: McGraw Hill, 1997 (see


Additional papers indicated by the instructor.


Recommended Readings


Russell S., and P. Norvig P., Learning, Chapter V (pp. 693-859) of Artificial Intelligence: A Modern Approach, Prentice Hall, Third edition ISBN-13: 978-0-13-604259-4, 2010.


Tecuci, G., Building Intelligent Agents: An Apprenticeship Multistrategy Learning Approach, Academic Press, 1998 (see


Michalski, R. S. and Tecuci, G. (editors). Machine Learning: A Multistrategy Approach, Volume 4, Morgan Kaufmann Publishers, San Mateo, CA, 1994.


Schum, D.A., Notes on Discovery and Imaginative Reasoning, George Mason University, Spring 2009.


Additional papers indicated by the instructor.


Email Communication

Please include CS782 in the subject of any message you are emailing to Dr. Tecuci.


GMU Email Accounts

Students must activate their GMU email accounts to receive important University information, including messages related to this class.


Office of Disability Services

If you are a student with a disability and you need academic accommodations, please see me and contact the Office of Disability Services (ODS) at (703) 993-2474. All academic accommodations must be arranged through the ODS.


Other Useful Campus Resources

Writing Center: A114 Robinson Hall; 703 993 1200;

University Libraries “Ask a Librarian”

Counseling And Psychological Services (CAPS):  703  993 2380;


University Policies

The University Catalog,, is the central resource for university policies affecting student, faculty, and staff conduct in university affairs. GMU is an Honor Code university. You are expected to abide by the University's honor code.