Bayesian machine learning methods can be used in production data to handle sparse and noisy pattern in production data. In case, if you are working on TensorFlow framework, then we can use ...

Abstract: This research study reviews the statistical fundamentals of machine learning with a focus on Bayesian methods to quantify the uncertainty in model predictions. Bayesian statistics provides a ...

Topics will be selected from: de Bruijn graphs in genomics, biological data compression, probabilistic models (HMM, SCFG, and MRF), graphical models and Bayesian approaches, information-theoretic ...

Abstract: Machine learning methods used in the field of bioinformatics are a frequently used solution method in diagnosing, treating and investigating the underlying causes of diseases. In addition, ...

How humans and computers can learn from data is a core question of both statistics and machine learning. Bayesian methods are widely used in these fields, yet they have certain limitations and ...

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 ...