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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. | |
Fall 2022 Industrial Engineering and Operations Research E4540 section 001 DATA MINING | |
| Call Number | 11692 |
| Day & Time Location |
W 7:10pm-9:40pm 303 Seeley W. Mudd Building |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Krzysztof M Choromanski |
| Type | LECTURE |
| 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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