To develop a machine learning algorithm for improving throughput in the new SAG - let's call it SAG No.1 - data scientists build a model that behaves according to things known to be true, such as ...

A machine learning model can manipulate data to find relationships, patterns and provide the data-based means to make predictions without probability. Commercial applications of "AI," which is a ...

This repository is a collection of notebooks about Bayesian Machine Learning.The following links display some of the notebooks via nbviewer to ensure a proper rendering of formulas. Dependencies are ...

The course will introduce the basic principles and algorithms used in Bayesian machine learning. This will include the Bayesian approach to regression and classification tasks, introduction to the ...

Bayesian Learning with Unbounded Capacity from Heterogenous and Set-Valued Data (AOARD, 2016-2018) Project lead: Prof. Dinh Phung. Large-scale and modern datasets have reshaped machine learning ...

A 250-year-old mathematical theory could be used to create ‘self-aware’ machine learning systems that understand when they are out of their depth, according to a panel of senior quants. Bayes’ theorem ...

D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press 2012; Assessment. Exam (50%, duration: 2 hours) in the summer exam period. Project (50%) in the ST. Course selection ...