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Course Criteria
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3.00 Credits
Explores supervised learning, linear methods for regression and classification, model assessment and selection, model inference and averaging, additive models, boosting, neural networks, support vector machines, and unsupervised learning. Prerequisites: C- or better in MATH 5720 and STAT 5100. Programming experience in R or a related language is strongly recommended.
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3.00 Credits
This course provides a practical and mathematical introduction to machine learning techniques and principles in supervised and unsupervised settings. Students learn to understand machine learning research papers and gain the practical experience to implement machine learning approaches on real data. Prerequisites/Restrictions: Graduate standing or: MATH 1220 STAT 3000 or MATH 5710 (MATH 5710 preferred) MATH 2210 or MATH/STAT 5645/6645 (MATH/STAT 5645/6645 preferred) MATH 2270 or MATH/STAT 5645/6645 (MATH/STAT 5645/6645 preferred) Experience programming in Python, R, or Matlab is essential for success in the course Cross listed as: CS 6655
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2.00 Credits
Students learn what Big Data is and how to visualize it. Topics include MapReduce, estimation and the bag of little bootstraps, and hypothesis testing, linear regression, classification, clustering, and nonlinear regression for Big Data. Prerequisites: STAT 5050 with a C- or better STAT 5100 with a C- or better STAT 5650 with a C- or better
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3.00 Credits
regularization, and universality. Topics include CNNs, GANs, RNNs, GCNs, autoencoders, transformers, and other modern architectures and training techniques. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite Recommendation(s): The following courses or their equivalents are necessary to succeed in this course: MATH 1220; MATH 2270 or MATH 5645/STAT 5645 or MATH 6645/STAT 6645; STAT 3000 or MATH 5710; Programming experience, preferably in Python, is also necessary to succeed in this course Dual-listed as: STAT 5685 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
This course is an advanced mathematical statistics course covering the basics of measure-theoretic probability, random variables and random vectors, generating functions, and modes of stochastic convergence. Prerequisite: STAT 5710 with a C- or better
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3.00 Credits
This course is an advanced mathematical statistics course covering the basics of measure-theoretic probability, random variables and random vectors, generating functions, and modes of stochastic convergence. Prerequisite: C- or better in STAT 6710.
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1.00 - 3.00 Credits
Topics vary by instructor each time course is offered. Prerequisite/Restriction: Permission of instructor Repeatable for credit.
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1.00 - 3.00 Credits
Topics vary by instructor each time the course is offered. Prerequisite/Restriction: Permission of instructor. Repeatable for credit.
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1.00 - 3.00 Credits
Review of current literature and developments in statistics. Prerequisite/Restriction: Permission of instructor. Repeatable for credit.
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1.00 - 4.00 Credits
This course consists of directed readings on specific topics. Prerequisite/Restriction: Prior arrangement with specific instructor. Repeatable for credit.
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