This course is available on the MSc in Applied Social Data Science, MSc in Data Science, MSc in Quantitative Methods ... machine learning. Several techniques that are probabilistic in nature are ...

Machine Learning ... Bayesian approach would be a Hierarchical Constraint Resolution Model. An excellent introduction to Hierarchical Constraint modeling is Solving Hierarchical Constraints over ...

Hence, we have applied a new machine learning method that disentangles the efficacy ... The computational biologist initiated the study and heads a bioinformatics research group at the Würzburg ...

The development of new bioinformatics ... and machine learning techniques including linear and nonlinear regression, principal component analysis, support vector machines, self-organizing maps, neural ...

Pearl figured out how to do that using a scheme called Bayesian networks ... headlines tout the latest breakthroughs in machine learning and neural networks. We read about computers that can ...

The Mining Lab aims to build statistical models to tackle hard learning problems with limited labels in knowledge-rich domain (e.g., medicine and bioinformatics). Two central research themes: - ...

The course sets up the foundations and covers the basic algorithms covered in probabilistic machine learning ... topics are revisited from a Bayesian viewpoint. The module provides training in ...