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

CS 782 Machine Learning

Meeting time: Thursday 7:20pm 10pm

Meeting location: ST-II, Room 430A


Instructor: Dr. Gheorghe Tecuci, Professor of Computer Science

Office hours: Monday and Thursday, 6pm-7pm, for both CS580 and CS782

Office: ST-II, Room 421

Phone: 703 993 1722



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: foundations of machine learning, rote learning, empirical inductive learning from examples, explanation-based learning, abductive learning, instance-based learning, case-based reasoning and learning by analogy. 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 tasks 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 problem solving agent developed in the Learning Agents Laboratory.

This course can be used as a Ph.D. comprehensive course.

Grading Policy

There will be assignments, 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, which may be of high interest to PhD students, requires an early submission of a proposal.

The course grade will be determined as follows:
    Assignments 5%
    Mid-term exam 50%
    Final exam or project 40%
    Class participation 5%
    Extra credit:
        Assignment3 10%


Lecture notes provided by the instructor (required).

Tom Mitchell, Machine Learning, New York: McGraw Hill, 1997 (recommended).

Additional relevant references

Buchanan, B. G. and Wilkins, D. C. (editors), (1993). Readings in Knowledge Acquisition and Learning: Automating the Construction and Improvement of Expert Systems, Morgan Kaufmann, San Mateo, CA.

Shavlik, J. W. and Dietterich, T. (editors), (1990). Readings in Machine Learning, Morgan Kaufmann.

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

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

Tecuci, G. and Kodratoff, Y. (editors), (1995). Machine Learning and Knowledge Acquisition: Integrated Approaches, Academic Press.

Exams and Assignments

10/16/2003    Midterm Exam (new date)

Assignment 1 (due date: 10/9/2003)

Assignment 2 (due date: 10/16/2003) 

Assignment 3 (due date: anytime before 11/20/2003)

Lecture Notes


Rote Learning

Inductive Learning from Examples: Version Space Learning

Inductive Learning from Examples: Decision Tree Learning

Evaluation of Inductive Learners

Explanation-based Learning

Abductive Learning

Learning by Analogy

Instance-based Learning

Case-based Reasoning and Learning

Multistrategy Learning
    Paper: An inference-based framework for multistrategy learning

Learning and Problem Solving Agents