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Fall 2020 Electrical Engineering and Computer Science E6720 section 001
BAYESIAN MOD MACHINE LEARNING
BAYESIAN MOD MACHINE LEAR

Call Number 10733
Day & Time
Location
W 4:10pm-6:40pm
417 International Affairs Building
Points 3
Grading Mode Standard
Approvals Required None
Instructor John W Paisley
Type LECTURE
Method of Instruction Hybrid
Course Description Prerequisites: Basic calculus, linear algebra, probability, and programming. Basic statistics and machine learning strongly recommended. Bayesian approaches to machine learning. Topics include mixed-membership models, latent factor models, Bayesian nonparametric methods, probit classification, hidden Markov models, Gaussian mixture models, model learning with mean-field variational inference, scalable inference for Big Data. Applications include image processing, topic modeling, collaborative filtering and recommendation systems.
Web Site Vergil
Department Electrical Engineering
Enrollment 91 students (150 max) as of 8:03AM Wednesday, October 21, 2020
Subject Electrical Engineering and Computer Science
Number E6720
Section 001
Division School of Engineering and Applied Science: Graduate
Open To Engineering:Undergraduate, Engineering:Graduate, GSAS
Campus Morningside
Section key 20203EECS6720E001

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SIS update 10/21/20 08:03    web update 10/21/20 11:19