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Course Criteria
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3.00 Credits
Credits: 3 Cross-Listed with OR 719/CSI 775 Introduces theory and methods for building computationally efficient software agents that reason, act, and learn environments characterized by noisy and uncertain information. Covers methods based on graphical probability and decision models. Students study approaches to representing knowledge about uncertain phenomena, and planning and actingunder uncertainty. Topics include knowledge engineering, exact and approximate inference in graphical models, learning in graphical models, temporal reasoning, planning, and decision making. Practical model building experience is provided. Students apply what they learn to semester-long project of their choosing. Prerequisites STAT 652 or SYST/STAT 664, 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 CSI 771 Covers basic computationally intensive statistical methods and related methods, which would not be feasible without modern computational resources. Covers nonparametric density estimation including kernel methods, orthogonal series methods and multivariate methods, recursive methods, cross validation, nonparametric regression, penalized smoothing splines, the jackknife and bootstrapping, computational aspects of exploratory methods including the grand tour, projection pursuit, alternating conditional expectations, and inverse regression methods. Prerequisites STAT 544, STAT 554, and STAT 652. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered AF
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3.00 Credits
Credits: 3 Statistical approach to computer intrusion detection. Networking basics, TCP/IP networking, network statistics, evaluation, intrusion detection, network monitoring, host monitoring, computer viruses and worms, Trojan programs and covert channels. Prerequisites STAT 554 or 663, or permission of instructor. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered IR
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3.00 Credits
Credits: 3 Mathematical modeling and methods for model identification and forecasting of nonstationary and seasonal time series data (ARIMA models), multivariate time series, and state-space models. Prerequisites STAT 658. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered AS
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3.00 Credits
Credits: 3 Advanced statistical methods in the drug development process. Provides the theoretical statistical basis for the design and analysis of pharmaceutical clinical trials. Topics include the theory of randomization, randomization-based inference, restricted, response-adaptive, and covariate-adaptive randomization, the modern theory of group sequential monitoring, statistical aspects of determination of dose-response relationships. Prerequisites STAT 544, STAT 652, working knowledge of statistical programming language. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered AF
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1.00 - 3.00 Credits
Credits: 1-3 Specialized advanced topics in survey sampling building on foundations in STAT 574 and 674. Topics vary according to interest and availability of instructors but may include administrative records in analysis of data, adaptive sampling, calibration estimators, capture-recapture models, data security, establishment surveys, model-based inference, measurement error models, nonresponse models, imputation, multivariate analysis of survey data, record linkage, small area estimation, spatial sampling, survey errors and costs, telephone survey methods, variance estimation, web-based survey methods. Prerequisites STAT 674 or permission of instructor. Notes Topics may be offered in form of modules from 1 to 3 credits, with 1-credit module offered over five weeks. Up to three modules may be offered in single semester for maximum 3 credits. Students may earn up to 6 credits under different topics. Hours of Lecture or Seminar per week 1-3 Hours of Lab or Studio per week 0 When Offered IR
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3.00 Credits
Credits: 3 Cross-Listed with SYST 781 Statistical and computational methods and systems for deriving user-oriented knowledge from large databases and other information sources, and applying knowledge to support decision making. Information sources can be in numerical, textual, visual, or multimedia forms. Covers theoretical and practical aspects of current methods and selected systems for data mining, knowledge discovery, and knowledge management, including those for text mining, multimedia mining and web mining. Prerequisites One of the following courses: CS 687, CS 650, INFS 614, STAT 663, STAT 664, or permission of instructor. Notes Content may vary from semester to semester. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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1.00 - 6.00 Credits
Credits: 1-6 Topics in statistics not covered in regular statistics sequence. Prerequisites Permission of instructor. Notes May be repeated for credit. Hours of Lecture or Seminar per week 1-6 Hours of Lab or Studio per week 0 When Offered IR
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1.00 - 3.00 Credits
Credits: 1-3 Reading and research on specific topic in statistics under direction of faculty member. Prerequisites Admission to PhD in Statistical Science Program. Hours of Lecture or Seminar per week 0 Hours of Lab or Studio per week 0
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1.00 - 3.00 Credits
Credits: 1-3 Reading and research on specific topic in statistics under direction of faculty member. Prerequisites Admission to PhD in Statistical Science Program. Hours of Lecture or Seminar per week 0 Hours of Lab or Studio per week 0
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