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Spring 2020 Industrial Engineering and Operations Research E4540 section 001

Call Number 15127
Day & Time
M 7:10pm-9:40pm
451 Computer Science Building
Points 3
Grading Mode Standard
Approvals Required None
Instructor Krzysztof M Choromanski
Course Description The course will cover major statistical learning methods for data mining under both supervised and unsupervised settings. Topics covered include linear regression and classification, model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. Students will learn about the principles underlying each method, how to determine which methods are most suited to applied settings, concepts behind model fitting and parameter tuning, and how to apply methods in practice and assess their performance. We will emphasize roles of statistical modeling and optimization in data mining.
Web Site Vergil
Department Industrial Engineering and Operations Research
Enrollment 28 students (80 max) as of 4:38PM Thursday, July 2, 2020
Subject Industrial Engineering and Operations Research
Number E4540
Section 001
Division School of Engineering and Applied Science: Graduate
Campus Morningside
Section key 20201IEOR4540E001

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SIS update 07/02/20 16:38    web update 07/02/20 17:19