This tutorial text gives a unifying perspective on machine learning by covering both
probabilistic and deterministic approaches-which are based on optimization techniques …

This monograph aims at providing an introduction to key concepts, algorithms, and
theoretical results in machine learning. The treatment concentrates on probabilistic models …

A detailed and up-to-date introduction to machine learning, presented through the unifying
lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and …

This paper describes and discusses Bayesian Neural Network (BNN). The paper showcases
a few different applications of them for classification and regression problems. BNNs are …

A comprehensive introduction to machine learning that uses probabilistic models and
inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for …

This self-contained introduction to machine learning, designed from the start with engineers
in mind, will equip students with everything they need to start applying machine learning …

Pattern recognition has its origins in engineering, whereas machine learning grew out of
computer science. However, these activities can be viewed as two facets of the same field …

The engineering of machine learning systems is still a nascent field; relying on a seemingly
daunting collection of quickly evolving tools and best practices. It is our hope that this …

Bayesian machine learning is a subfield of machine learning that incorporates Bayesian
principles and probabilistic models into the learning process. It provides a principled …

Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random
variables to infinite (countably or continuous) index sets. GPs have been applied in a large …