Lehrinhalte
Logic programming
Inductive logic programming, i.e., learning logical programs from data
Probabilistic graphical models: Inference and Learning
Statistical relational models such as ProbLog and Markov logic networks
Inference within statistical relational models
Learning statistical relational models from data
Relational linear and quadratic programs
Literatur
Pointers to literature will be updated regularly and include:
Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole (2016): Statistical Relational Artificial Intelligence: Logic, Probability, and Computation. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 9781627058414.
Voraussetzungen
The successful completion of Statistical Machine Learning and of Probabilistic Graphical Models is recommended but not required.
Online-Angebote
moodle
Logic programming
Inductive logic programming, i.e., learning logical programs from data
Probabilistic graphical models: Inference and Learning
Statistical relational models such as ProbLog and Markov logic networks
Inference within statistical relational models
Learning statistical relational models from data
Relational linear and quadratic programs
Literatur
Pointers to literature will be updated regularly and include:
Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole (2016): Statistical Relational Artificial Intelligence: Logic, Probability, and Computation. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 9781627058414.
Voraussetzungen
The successful completion of Statistical Machine Learning and of Probabilistic Graphical Models is recommended but not required.
Online-Angebote
moodle
- Lehrende: Kristian Kersting
Semester: Inverno 2019/20