EECS E6720
BAYESIAN MOD MACHINE LEARNING
Lect: 3. 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
Topics
- 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
Topics explicitly listed in official catalogue description
Prerequisites & course information
Prerequisites: Basic calculus, linear algebra, probability, and programming.
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- BAYESIAN MOD MACHINE LEARNING · 3.00 points · University catalogue