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Spring 2014 Computer Science W4771 section 001
|Day & Time
To be announced
|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.|
|Enrollment||88 students (100 max) as of 11:21PM Tuesday, December 10, 2013|
|Open To||Columbia College, Engineering and Applied Science, General Studies, Graduate School of Arts and Science, Engineering and Applied Science: Graduate, Barnard|
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