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IT/CS 811 Principles of Machine Learning and Inference

IT/CS 811 Principles of Machine Learning and Inference. 2. Projects and assignments. Prof. Gheorghe Tecuci. Learning Agents Laboratory Computer Science Department George Mason University. Individual or group projects. Journal paper. Research report.

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IT/CS 811 Principles of Machine Learning and Inference

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  1. IT/CS 811 Principles of Machine Learning and Inference 2. Projects and assignments Prof. Gheorghe Tecuci Learning Agents Laboratory Computer Science Department George Mason University

  2. Individual or group projects Journal paper Research report Overview of a special machine learningand/or inference topic Development of a learning and reasoning agent Other

  3. Example project: Journal paper • Cristina Boicu • Exception-driven knowledge base refinement • Prerequisites • significant part of the research already done (e.g. system development); • potential of theoretical and/or experimental results. • Approach • start with selecting the journal; • develop preliminary table of context during the first two weeks; • schedule section writing during each week; • - have paper ready for review two weeks prior to project deadline; • - paper ready for submission by the project deadline.

  4. Example project: Research report (journal potential) • An overview of interactive learning methods • Prerequisites • desire to perform an extensive literature search; • interest in detailed reading and comparison of several papers. • Approach • start with selecting an appropriate journal; • perform literature search; • develop comparison criteria and preliminary table of context; • schedule paper reading and summarization during each week; • - have paper ready for review two weeks prior to project deadline.

  5. Ex. project: Development of a learning and reasoning agent • An assistant for selecting a PhD advisor • (could be a single person or a group project) • Approach • start from the assistant developed in IT803/Spring 2002; • develop a consistent object ontology; • develop elicitation scripts for students, advisors, PhD coordinator; • train the agent; • write a report/paper. • (to be detailed based on the number of student participants)

  6. Sample assignments Review a project journal paper or reportand present the review in class Literature search on the applications of a specific learning method (e.g. decision tree learning) and class presentation Develop an interesting exercise based on somelearning algorithm (suitable for the closed book partof the final exam) and class presentation Read an interesting paper on learning and inference and present it to the class Others • Approach • each assignment has a different number of points (based on the work required) • accumulate 100 points in assignments.

  7. Grading policy discussion • 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, • 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%

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