NOTE: Course information changes frequently, including Methods of Instruction. Please revisit these pages periodically for the most recent and up-to-date course information. | |
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 | 90 students (150 max) as of 4:03PM Wednesday, April 21, 2021 |
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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