… Thus there are many different kinds of machine learning, … types of ML, but from a probabilistic
perspective. Roughly speaking, this … There are two main reasons we adopt a probabilistic …

… This Review starts with an introduction to the probabilistic approach to machine learning
and … Many aspects of learning and intelligence crucially depend on the careful probabilistic …

… , which is what machine learning provides. In particular, we define machine learning as a set
… In machine learning, uncertainty comes in many forms: what is the best prediction about the …

… learning into a larger statistical context and unifies approaches based on deep learning with
ones based on probabilistic … the vital issues in machine learning. Covers generation of high …

… Probabilistic methods are the heart of machine learning. This chapter shows links between
core principles of information theory and probabilistic … Probabilistic models are introduced as …

… This book introduces probabilistic machine learning concepts to … the more advanced machine
learning literature from which this … of probabilistic machine learning: supervised learning, …

… building pipeline where probabilistic models can be … probabilistic machine learning models
in phenotyping, in generative models for clinical use cases, and in reinforcement learning…

… -art machine learning (ML) technologies are integral in optimizing decision-making in energy
distribution networks and systems. This study was conducted on data-driven probabilistic …

… is the application of probabilistic machine learning for predicting … This probabilistic model
is able to replicate the dominant … The probabilistic machine learning model is introduced and …

… and on the reason for using probabilistic models (since they are all … (sometimes favoring
“machine learning” references over “… (“The probabilistic approach to machine learning is closely …