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Course Criteria
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3.00 Credits
Credits: 3 This case study-grounded seminar introduces student team members to the unique complexities of the federal sector, including congressional and executive branch oversight and the reporting, justifying, and sustaining annually very large IT programs. 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 CS 667 Basic principles and methods for automatic authentication of individuals. Technologies include face, fingerprint, iris recognition, and speaker verification, among others. Additional topics cover multimodal biometrics, system design, performance evaluation, and privacy concerns.Term project required. Prerequisites CS 580 or permission of the instructor.
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3.00 Credits
Credits: 3 Faculty-facilitated, student teams analyze business cases from perspectives of IT, management and analysis, as well as leadership and ethics. 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 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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3.00 Credits
Credits: 3 Cross-Listed with OR 735/ SYST 735 Special topics and recent developments in Monte Carlo simulation methodology for discrete-event stochastic systems. Contents vary; possible topics include statistical analysis of simulation output data, random number and random ariate generation, variance reduction techniques, sensitivity analysis and optimization of simulation models, distributed and parallel simulation, object-oriented simulation, and specialized applications. Prerequisites OR 635 or permission of instructor. Notes May be repeated for credit when topics are distinctly different. 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 CSI 776 For graduate students in information technology, electrical engineering, mathematics, operations research, and statistics. Introduction to modern theory of stochastic calculus such as stochastic integrals, martingales, counting processes, diffusion processes, and Ito-type processes in general. Presents applications of methods to engineering and biology. Focuses on developing necessary concepts rather than mathematical proofs. Prerequisites STAT 652 or CE 630 or 632. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered A, F
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3.00 Credits
Credits: 3 Cross-Listed with CS 750 Concepts and techniques in data mining and their 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 681, 687, or 688; or permission of instructor. Notes Term project and topical review. 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 SYST 763/OR 763 Examines alternative paradigms of scientific research and their applicability to research in information technology. Topics include fundamental elements of scientific investigation, basic principles of experimental design and statistical induction, philosophy of science and its relation to the information technology sciences, and case studies of information technology research. Prerequisites STAT 554
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3.00 Credits
Credits: 3 Cross-Listed with OR 774 This course covers advanced topics on the theory and practice of dynamic programming, i.e., optimal sequential decision making over time in the presence of uncertainties. The course will stress the mathematical foundations and will introduce the theory, computational aspect, and applications of dynamic programming for deterministic and stochastic problems. Prerequisites OR 674/SYST 674 or permission of the instructor.
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3.00 Credits
Credits: 3 Cross-Listed with CSI 778 Advanced calculus and linear algebra needed for doctoral work in statistics and related fields. Topology, vector spaces, atrices, continuity, differentiation, sequences and series of real numbers and real-valued functions, Riemann and Riemann-Stieltjes integrals, and multidimensional calculus. Presents applications in probability and statistics including response surface methodology. Prerequisites STAT 652; or ECE 620, 621, and 630. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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