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Course Criteria
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3.00 Credits
Credits: 3 Project chosen and completed under guidance of graduate faculty member that results in acceptable technical report. Prerequisites 9 graduate credits, and permission of instructor. Hours of Lecture or Seminar per week 0 Hours of Lab or Studio per week 0
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1.00 - 6.00 Credits
Credits: 1-6 Project chosen and completed under guidance of graduate faculty member that results in acceptable technical report and oral defense. Prerequisites 9 graduate credits, and permission of instructor. Hours of Lecture or Seminar per week 0 Hours of Lab or Studio per week 0
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3.00 Credits
Credits: 3 Cross-Listed with IT 871 Covers basic concepts, computational complexity, data preparation and compression, databases and SQL, rule-based machine learning and probability, density estimation, exploratory data analysis, cluster analysis and pattern recognition, artificial neural networks, classification and regression trees, correlation and nonparametric regression, time series, and visual data mining. 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 Cross-Listed with IT 875 Covers visualization methods used to provide new insights and intuition concerning measurements of natural phenomena, and scientific and mathematical models. Presents case studies from myriad disciplines. Topics include human perception and cognition, introduction to graphics laboratory, elements of graphing data, representation of space-time and vector variables, representation of three-dimensional and higher dimensional data, dynamic graphical methods, and virtual reality. Work on a visualization project required. Emphasizes software tools on Silicon Graphics workstation, but other workstations and software may be used. Prerequisites CS 652, STAT 554, STAT 663, or STAT 751; or permission of instructor. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered S
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3.00 Credits
Credits: 3 Cross-Listed with IT 876/CSI 876 Measure theory and integration; convergence theorems; theory of linear spaces and functional analysis; and probability theory. The theory of linear spaces includes normed linear spaces, inner product spaces, Banach and Hilbert spaces, Sobelev spaces, and reproducing kernels. Topics include wavelets, applications to stochastic processes, and nonparametric functional inference. Prerequisites STAT 544 and MATH 315. 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 Cross-Listed with IT 877/CSI 877 Develops foundations of geometric methods for statistics. Topics include n-dimension Euclidian geometry; projective geometry; differential geometry, including curves, surfaces, and n-dimensional differentiable manifolds; and computational geometry, including computation of convex hulls, tessellations of two-, three-, and n-dimensional spaces, and finite element grid generation. Examples include applications to scientific visualization. Prerequisites STAT 751 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 Cross-Listed with IT 971 A rigorous measure-theoretic treatment of probability. Includes expectation, distributions, laws of large numbers and central limit theorems for independent random variables, characteristic function methods, conditional expectations, martingales, strong and weak convergence, and Markov chains. Prerequisites STAT 544 and MATH 315. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered S
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3.00 Credits
Credits: 3 Cross-Listed with IT 972/CSI 972 Focuses on theory of estimation. Includes method of moments, least squares, maximum likelihood, and maximum entropy methods. Details methods of minimum variance unbiased estimation. Topics include sufficiency and completeness of statistics, Fisher information, Cramer-Rao bounds, Bhattacharyya bounds, asymptotic consistency and distributions, statistical decision theory, minimax and Bayesian decision rules, and applications to engineering and scientific problems. Prerequisites STAT 652/CSI 672 or equivalent, and either STAT 876/IT 876/CSI 876 or STAT 971/IT 971. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered F
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3.00 Credits
Credits: 3 Cross-Listed with IT 973/CSI 973 Continuation of STAT 972/IT 972/CSI 972. Concentrates on theory of hypothesis testing. Topics include characterizing decision process, simple versus simple hypothesis tests, Neyman-Pearson Lemma, uniformly most powerful tests, unbiasedness and invariance of tests, and randomized and sequential tests. Applications of testing principles made to situations in normal distribution family and other families of distributions. Prerequisites STAT 972/IT 972/CSI 972. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered S
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1.00 - 12.00 Credits
Credits: 1-12 Work on research proposal that forms basis for doctoral dissertation. Notes May be repeated. No more than 24 credits of STAT 998 and 999 may be applied to doctoral degree requirements. Hours of Lecture or Seminar per week 0 Hours of Lab or Studio per week 0
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