| Call Number | 11563 |
|---|---|
| Day & Time Location |
W 4:10pm-6:40pm To be announced |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | John W Paisley |
| Type | LECTURE |
| Method of Instruction | In-Person |
| Course Description | 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 | 0 students (50 max) as of 1:06PM Thursday, October 9, 2025 |
| 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 |
| Section key | 20261EECS6720E001 |