… I highlight five areas of current research at the frontier of probabilistic machine learning, … :
probabilistic programming, which is a general framework for expressing probabilistic models as …

… variables, that are endowed with probability distributions which describe a weighted set of
… of probability, if necessary.) There are two main reasons we adopt a probabilistic approach. …

… using machine learning. 1 The field of artificial intelligence (AI) includes the field of machine
… the Venn diagram in figure 1.2, machine learning is part of the field of artificial intelligence. …

… probability into what are called relational probabilistic models. It introduces representations,
inference, and learning techniques for probability, … Problog, a probabilistic extension of logic …

… New state-of-the-art machine learning (ML) technologies are … This study was conducted
on data-driven probabilistic ML … ML is a subset of artificial intelligence (AI) that applies …

… of an artificial neural network. Deep learning is basically just a neural network with many …
However, this statement alone does not pay justice to the enormous impact deep learning has …

… probability of uncertain observations to be the probability of the underlying event. We show
that considering probability upper bound as the probability … In probabilistic machine learning, …

… using more sophisticated machine learning systems for … probabilistic, stochastic and artificial
intelligence methods present a deep insight into the prospects of using artificial intelligence …

… for explaining the prediction of probabilistic machine learning algorithms using cases. The
… to a probability model and an novel case-based approach to justifying the probabilistic …

… With the development of artificial intelligence, machine learning methods are widely used
in the field of civil engineering. For example, machine learning has been used in structural …