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  • CS 685: Intelligent Systems for Robots

    3.00 Credits
    George Mason University

    Credits: 3 Reviews developments in intelligent autonomous systems. Studies applications of artificial intelligence, computer vision, and machine learning to robotics. Topics include analysis and design of algorithms and architectures for planning, navigation, sensory data understanding, sensor fusion, spatial reasoning, motion control, knowledge acquisition, learning concepts and procedures, self-organization, and adaptation to environment. Prerequisites One of CS 580, ECE 650, SYST 611 or 555, or equivalent. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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  • CS 686: Image Processing and Applications

    3.00 Credits
    George Mason University

    Credits: 3 Concepts and techniques in image processing. Discusses methods for image capture, transformation, enhancement, restoration, and encoding. Students complete projects involving naturally occurring images. Prerequisites CS 583 and either STAT 344 or MATH 351, or equivalent. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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  • CS 687: Advanced Artificial Intelligence

    3.00 Credits
    George Mason University

    Credits: 3 Explores foundational issues of artificial intelligence, such as roles of knowledge and search, formalization of knowledge and inference, and symbolic versus emergent approaches to intelligence. Studies advanced programming techniques for artificial intelligence, relationship to foundational issues, and important application areas for artificial intelligence. Prerequisites CS 580. Notes Major programming project required. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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  • CS 688: Pattern Recognition

    3.00 Credits
    George Mason University

    Credits: 3 Cross-Listed with IT 688 Explores statistical pattern recognition and neural networks. Pattern recognition topics include Bayesian classification and decision theory, density (parametric and nonparametric) estimation, linear and nonlinear discriminant analysis, dimensionality reduction, feature extraction and selection, mixture models and EM, and vector quantization and clustering. Neural networks topics include feed-forward networks and back-propagation, self-organization feature maps, and radial basis functions. Course emphasizes experimental design, applications, and performance evaluation. Prerequisites CS 580 or equivalent. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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  • CS 695: Topics in Computer Science

    3.00 Credits
    George Mason University

    Credits: 3 Special topics in computer science not occurring in regular computer science sequence. Prerequisites Completion of two core courses, and permission of instructor. Notes May be repeated for credit when subject distinctly different. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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  • CS 697: Independent Reading and Research

    1.00 - 3.00 Credits
    George Mason University

    Credits: 1-3 In areas of importance but insufficient demand to justify a regular course, students may undertake a course of study under supervision of consenting faculty member. Students usually submit written statement of course content and tentative reading list as part of request for approval. Literature review, project report, or other written product usually required. Prerequisites Graduate standing; completion of at least two of core courses CS 540, 571, 580, and 583; and permission of instructor. Hours of Lecture or Seminar per week 0 Hours of Lab or Studio per week 0
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  • CS 700: Quantitative Methods and Experimental Design in Computer Science

    3.00 Credits
    George Mason University

    Credits: 3 Integrated treatment to models and practices of experimental computer science. Topics include scientific methods applied to computing, workload characterization, forecasting of performance and quality metrics of systems, uses of analytic and simulation models, design of experiments, interpretation and presentation of experimental results, hypothesis testing, and statistical analyses of data. Involves one or more large-scale projects. Prerequisites STAT 344, at least two 600-level courses in computer science, and doctoral status. Hours of Lecture or Seminar per week 0 Hours of Lab or Studio per week 0
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  • CS 706: Concurrent Software Systems

    3.00 Credits
    George Mason University

    Credits: 3 Topics include concurrent programming languages and constructs, and specification, design, verification, and validation of concurrent programs. Students required to solve concurrent programming problems and check solutions by using verification, testing, and debugging tools. Prerequisites CS 571 and SWE 621 or 631, or equivalent Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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  • CS 719: Scaling Technologies for E-business

    3.00 Credits
    George Mason University

    Credits: 3 Cross-Listed with IT 809 Discusses, from quantitative point of view, characteristics of most important technologies used to support implementation of e-business sites. Includes topics such as hardware and software architectures of e-business sites, authentication, payment services, understanding customer behavior, workload characterization, scalability analysis, and performance prediction. Prerequisites At least one operating system and one networking course, and admission to VSITE doctoral program. Notes Term paper and project required. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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  • CS 732: Software Maintenance and Reuse

    3.00 Credits
    George Mason University

    Credits: 3 Cross-Listed with IT 822 Perfective maintenance, reuse of software components and patterns, evolving software systems, principles of object-oriented analysis and development. Presents issues regarding technologies supporting perfective software maintenance and reuse. Prerequisites CS/SWE 621 or equivalent, data structures, principles of modern programming, and discrete mathematics; or permission of instructor. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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