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Fall 2022 Computer Science W4771 section 001
MACHINE LEARNING

Call Number 11027
Day & Time
Location
TR 2:40pm-3:55pm
833 Seeley W. Mudd Building
Points 3
Grading Mode Standard
Approvals Required None
Instructor Nakul Verma
Type LECTURE
Method of Instruction In-Person
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 Vergil
Department Computer Science
Enrollment 113 students (120 max) as of 8:06AM Wednesday, December 7, 2022
Subject Computer Science
Number W4771
Section 001
Division Interfaculty
Campus Morningside
Section key 20223COMS4771W001

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SIS update 12/07/22 08:06    web update 12/07/22 08:31