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

IT 811 Principles of Machine Learning and Inference

 

Meeting time: Monday 7:20pm – 10pm

Meeting location: ST-II, Room 430A

 

Instructor: Dr. Gheorghe Tecuci, Professor of Computer Science

Office hours: Monday  6:15pm – 7:15pm
Office: ST-II, Rm. 421
Phone: 993-1722
E-mail: tecuci@ gmu.edu

Course Description

 

Prerequisite: an introductory course in artificial intelligence or permission of instructor.

 

This course presents the principles, strategies, major methods, systems, applications, open issues and research directions in machine learning and inference. Covered topics include rote learning, inductive learning, deductive learning, abductive learning, learning and reasoning by analogy, case-based reasoning and learning, instance-based learning, conceptual clustering, quantitative discovery, reinforcement learning, genetic algorithms, and neural networks. The relative strengths and weaknesses of these strategies, as well as their most appropriate application domains will be discussed. The course will also cover more recent topics such as multistrategy learning, automated knowledge acquisition, integrated teaching and learning, and instructable agents. Interested students may work on projects relevant to their research area or may experiment with or enhance the learning software developed in the Learning Agents Laboratory.

 

Grading Policy

 

There will be several assignments, a final exam and an optional project. The final exam will contain two parts, a closed book one containing theoretical questions, and an open book one containing problems. The open book part of the exam may be substituted by a project. The final grade will be computed as follows:

  • Class participation and assignments: 20%
  • Closed book part of the final exam: 30%
  • Open book part of the final exam or the project: 50%

 

Readings

  • 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.
  • 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.

 

 

Preliminary Schedule