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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. | |
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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