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NOTE: Course information changes frequently, including Methods of Instruction. Please revisit these pages periodically for the most recent and up-to-date course information.


Summer 2022 Computer Science W4771 section 001
MACHINE LEARNING

Call Number 10340
Day & Time
Location
TR 1:00pm-4:10pm
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
Subterm 07/05-08/12 (B)
Department Computer Science
Enrollment 50 students (120 max) as of 2:11PM Friday, May 20, 2022
Subject Computer Science
Number W4771
Section 001
Division Interfaculty
Campus Morningside
Section key 20222COMS4771W001

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SIS update 05/20/22 14:11    web update 05/20/22 14:35