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Fall 2022 Industrial Engineering and Operations Research E4540 section 001

Call Number 11692
Day & Time
W 7:10pm-9:40pm
303 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 47 students (73 max) as of 6:09PM Wednesday, March 29, 2023
Subject Industrial Engineering and Operations Research
Number E4540
Section 001
Division School of Engineering and Applied Science: Graduate
Campus Morningside
Section key 20223IEOR4540E001

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SIS update 03/29/23 18:09    web update 03/29/23 18:19