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Spring 2013 Computer Science W4771 section 001 MACHINE LEARNING | |
| Call Number | 13748 |
| Day & Time Location |
TR 1:10pm-2:25pm 702 Hamilton Hall |
| Points | 3 |
| Approvals Required | None |
| Instructors | Adrian V Weller Ilia Vovsha |
| Type | LECTURE |
| 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 | CourseWorks |
| Department | Computer Science |
| Enrollment | 64 students (86 max) as of 11:21PM Monday, May 20, 2013 |
| Final Exam Day/Time | May 14 T 1:10pm-4:01pm |
| Final Location | 717 Hamilton Hall |
| Subject | Computer Science |
| Number | W4771 |
| Section | 001 |
| Division | Interfaculty |
| Open To | Columbia College, Engineering and Applied Science, General Studies, School of Continuing Education, Graduate School of Arts and Science, School of the Arts, International and Public Affairs, Barnard, Engineering and Applied Science: Graduate |
| Campus | Morningside |
| Section key | 20131COMS4771W001 |
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