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Course Criteria
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3.00 Credits
Credits: 3 Studies techniques, tools for specifying and verifying concurrent and distributed programs. Topics may include model checking, temporal logic, process algebra, and test generation. Automated verification tools used to specify and verify concurrent programs. Prerequisites CS 635 or 706, or equivalent. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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3.00 Credits
Credits: 3 Cross-Listed with IT 750 Concepts and techniques in data mining and multidisciplinary applications. Topics include databases; data cleaning and transformation; concept description; association and correlation rules; data classification and predictive modeling; performance analysis and scalability; data mining in advanced database systems, including text, audio, and images; and emerging themes and future challenges. Prerequisites CS 688 or permission of instructor. Notes Term project and topical review required. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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3.00 Credits
Credits: 3 Advanced graphics methods and tools. Topics include visualization, modeling, rendering, animation, simulation, virtual reality, graphics software tools, and current research topics. Prerequisites CS 652. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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3.00 Credits
Credits: 3 Cross-Listed with IT 815 Covers topics illustrating contemporary thinking on architectures, application, development environments, algorithms, operating systems, language requirements, and performance. Prerequisites CS 635 or CSI 801. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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3.00 Credits
Credits: 3 Current and emerging issues in advanced computer networks and applications. Topics include software systems associated with packet and cell-switched networking architectures and protocols, high-performance LANs, scheduling and congestion control, mobile networking, multimedia applications, and next generation of Internet. Prerequisites CS 555. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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3.00 Credits
Credits: 3 Analytical and simulation techniques for modeling and analyzing computer networks. Examines elementary queuing analysis; networks of queues; routing and flow controls; and applications to local and wide area networks, Internet, and emerging networking technologies. Prerequisites CS 555. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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3.00 Credits
Credits: 3 Cross-Listed with IT 817 Studies adaptive and competitive principles using distributed and parallel computation. Topics include background from statistics, control, adaptive signal processing, and neurosciences. Basic models, such as those suggested by Grossberg, Hopfield, and Kohonen, discussed in terms of analytical characteristics and applications. Neural networks assessed as universal approximators. Connections to fuzzy approach established through the Radial Basis Function approach. Presents applications to perception, knowledge-based systems, and robotics. Prerequisites CS 688, or permission of instructor. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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3.00 Credits
Credits: 3 Real-time systems and principles supporting design and implementation. Emphasizes fundamental results from real-time scheduling theory and relevance to computer system design. Topics include system design issues for real-time applications involving communication networks, operating systems, databases, and multimedia. Prerequisites CS 555 or CS 671 or permission of the instructor. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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3.00 Credits
Credits: 3 Cross-Listed with IT 835 Studies recent advances in development of machine vision algorithms and knowledge-based vision systems. Topics include scalespace; Gabor and wavelet processing; distributed and hierarchical processing using neural networks; motion analysis; active, functional, and selective perception; object and target recognition; expert systems; data fusion; and machine learning. Emphasizes system integration in terms of perception, control, action, and adaptation. Presents applications to robotics, intelligent highways, inspection, forensic, and data compression. Prerequisites CS 68 and 686; or permission of instructor. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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3.00 Credits
Credits: 3 Cross-Listed with IT 844 Covers statistical pattern recognition, neural network, and statistical learning theory approaches. Topics include decision theory and Bayes' theorem, density (parametric and nonparametric) estimation, linear and nonlinear discriminant analysis, SVM and kernel methods, SRM and model selection, performance evaluation, mixture of experts (AdaBoost), dimensionality reduction, feature selection and extraction, and clustering. Emphasizes experimental design, applications, and performance evaluation. Prerequisites CS 688 or permission of instructor. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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