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NOTE: Course information changes frequently, including Methods of Instruction. Please revisit these pages periodically for the most recent and up-to-date course information.

Spring 2022 Industrial Engineering and Operations Research E4540 section 001

Call Number 13453
Day & Time
W 7:10pm-9:40pm
633 Seeley W. Mudd Building
Points 3
Grading Mode Standard
Approvals Required None
Instructor Krzysztof M Choromanski
Method of Instruction In-Person
Course Description Course covers 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 learn about 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. Emphasizes roles of statistical modeling and optimization in data mining.
Web Site Vergil
Department Industrial Engineering and Operations Research
Enrollment 27 students (70 max) as of 9:04AM Monday, November 28, 2022
Subject Industrial Engineering and Operations Research
Number E4540
Section 001
Division School of Engineering and Applied Science: Graduate
Open To Engineering:Undergraduate, Engineering:Graduate
Campus Morningside
Section key 20221IEOR4540E001

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SIS update 11/28/22 09:04    web update 11/28/22 09:24