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5825. Bayesian Machine Learning
3.00 credits
Prerequisites:
Grading Basis: Graded
Bayesian machine learning is a unifying methodology for reasoning about uncertainty when modelling complex data. This course begins by covering the foundations of probabilistic modelling, Monte Carlo and variational inference algorithms, and model checking. We build on these foundations by considering essential models, e.g., mixed-membership and hierarchical models, and their applications. The course concludes with a survey of recent advances in Bayesian machine learning focusing on Bayesian nonparametrics and other advanced topics.
Last Refreshed: 02-JAN-25 05.20.24.288918 AM
| Enrollment Data | Section | Class Number | Notes | Instructor | Enrollment | Session | Instruction Mode |
|---|---|---|---|---|---|---|---|
| 1248 16078 1 002 | 002 | 16078 | Aguiar, Derek | 5/5 | Reg | Online Synchronous |