Chalmers University of Technology
- Bayesian inference, probabilistic modeling of data
- Supervised learning: Bayesian linear regression
- Bayesian graphical models
- Monte Carlo techniques: importance sampling, Gibbs sampling, Markov Chain Monte Carlo
- Markov random fields, factor graphs
- Belief propagation, variable elimination
- Hidden Markov models
- Expectation propagation and variational inference
- Gaussian processes
- Unsupervised learning
- Generative adversarial networks and variational autoencoders: two methods for unsupervised learning,
- Probabilistic deep learning