Department of Computer Science
IT 811 Principles of Machine Learning and Inference
Meeting time: Monday
Meeting location: ST-II, Room 430A
Instructor: Dr. Gheorghe Tecuci, Professor of Computer Science
Office hours: Monday
Office: ST-II, Rm. 421
E-mail: tecuci@ gmu.edu
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.
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:
Additional relevant references
Neural network learning
Integrated teaching and learning