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Fall 2017 Computer Science W4771 section 001
|Day & Time
428 Pupin Laboratories
|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||113 students (130 max) as of 12:20AM Tuesday, September 19, 2017|
|Open To||School of the Arts, Barnard, Columbia College, Engineering and Applied Science: Undergraduate, Engineering and Applied Science: Graduate, Graduate School of Arts and Science, General Studies, Global Programs, International and Public Affairs|
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