NOTE: Course information changes frequently. Please re-visit these pages periodically for the most recent and up-to-date information.
Spring 2014 Computer Science W4771 section 001
|Day & Time
535 Seeley W. Mudd Building
|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||83 students (100 max) as of 6:49PM Saturday, March 8, 2014|
|Open To||Columbia College, Engineering and Applied Science: Undergraduate, General Studies, School of Continuing Education, Global Programs, Graduate School of Arts and Science, School of the Arts, International and Public Affairs, Barnard, Engineering and Applied Science: Graduate|
Home About This Directory Online Bulletins ColumbiaWeb