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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 Computer Science W4771 section 001 MACHINE LEARNING | |
| Call Number | 11027 |
| Day & Time Location |
TR 2:40pm-3:55pm 833 Seeley W. Mudd Building |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Nakul Verma |
| Type | LECTURE |
| Method of Instruction | In-Person |
| Course Description | Prerequisites: Any introductory course in linear algebra and any introductory course in statistics are both required. Highly recommended: COMS W4701 or knowledge of Artificial Intelligence. Topics from generative and discriminative machine learning including least squares methods, support vector machines, kernel methods, neural networks, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models and hidden Markov models. Algorithms implemented in MATLAB. |
| Web Site | Vergil |
| Department | Computer Science |
| Enrollment | 113 students (120 max) as of 9:08PM Friday, March 31, 2023 |
| Subject | Computer Science |
| Number | W4771 |
| Section | 001 |
| Division | Interfaculty |
| Campus | Morningside |
| Section key | 20223COMS4771W001 |
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