Spring 2023 Industrial Engineering and Operations Research E4540 section 001


Call Number 11498
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 23 students (60 max) as of 8:44PM Wednesday, February 28, 2024
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 20231IEOR4540E001