Course Contents
- Refresher of probability & Bayesian decision theory
- Directed and undirected models and their properties
- Inference in tree graphs
- Approximate inference in general graphs: Message passing and mean field
- Learning of directed and undirected models
- Sampling methods for learning and inference
- Modeling in example applications, including topic models
- Deep networks
- Semi-supervised learning
Literature
Literature recommendations will be updated regularly, an example might be:
- D. Barber: Bayesian Reasoning and Machine Learning, Cambridge University Press 2012
- D. Koller, N. Friedman: Probabilistic Graphical Models: Principles and Techniques, MIT Press 2009
Preconditions
Recommended: Participation in Statistisches Maschinelles Lernen.
Bemerkung Webportal
[b]Area:[/b] Human Computer Systems<br /> <br />
Online Offerings
moodle
- Refresher of probability & Bayesian decision theory
- Directed and undirected models and their properties
- Inference in tree graphs
- Approximate inference in general graphs: Message passing and mean field
- Learning of directed and undirected models
- Sampling methods for learning and inference
- Modeling in example applications, including topic models
- Deep networks
- Semi-supervised learning
Literature
Literature recommendations will be updated regularly, an example might be:
- D. Barber: Bayesian Reasoning and Machine Learning, Cambridge University Press 2012
- D. Koller, N. Friedman: Probabilistic Graphical Models: Principles and Techniques, MIT Press 2009
Preconditions
Recommended: Participation in Statistisches Maschinelles Lernen.
Bemerkung Webportal
[b]Area:[/b] Human Computer Systems<br /> <br />
Online Offerings
moodle
- Lehrende: Kristian Kersting
Semester: Inverno 2019/20