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Fall 2017 Computer Science W4771 section 001
MACHINE LEARNING

Call Number 76261
Day & Time
Location
MW 2:40pm-3:55pm
428 Pupin Laboratories
Points 3
Approvals Required None
Instructor James McInerney
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 88 students (130 max) as of 12:15AM Saturday, November 18, 2017
Subject Computer Science
Number W4771
Section 001
Division Interfaculty
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
Campus Morningside
Section key 20173COMS4771W001

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