Spring 2010: Advanced Pattern Recognition [CS775]
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Instructor:
Carlotta Domeniconi, Rm 4424 Engineering Building, carlotta@cs.gmu.edu
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Prerequisites:
CS 688 or permission of instructor.
Some programming experience is expected.
Students should be familiar with
basic probability and statistics concepts, linear algebra, optimization, and multivariate
calculus.
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Time:
We meet in ST1 122, M 4:30pm - 7:10pm
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Textbook: No required textbook. Reading material will be provided.
General Description and Preliminary List of Topics:
The course covers advanced topics in pattern recognition and machine learning.
Recent conference and journal papers will be discussed in depth.
Tentative topics: Mixture models and EM; Ensemble methods; Co-clustering;
Transfer learning; Semi-supervised learning; Learning with external knowledge;
Generative approaches to topic modeling; Kernel methods.
Actual topics covered will depend on time available and students' interests.
Course Format:
Lectures by the instructor, students' presentations, and discussions.
Research papers and handouts will be made available.
The course requires a project, homeworks and exams (exact format to be decided).
Homework assignments and project will require
some programming.
Course Project:
The project gives you an opportunity to explore in depth a particular topic/area of the course that interests you. The topic of the project, of course, should be related to the material covered in class, but otherwise you are free to select the specific topic. Possible types of projects include:
An application research project: The project demonstrates the application of some techniques discussed in class in an application domain (e.g., text mining, bioinformatics, computer vision, image processing, artificial intelligence etc.). Properties, drawbacks, advantages of the used techniques are analyzed within the context of the explored application domain.
A theoretical or methodological research project: A study of different classes of models and approaches; proving either theoretically or experimentally properties of known algorithms; designing a new approach.