Nematrian Reference Library
[this page | back links]
Set out below is information (held by the Nematrian website) on the reference you have selected
Pages on this website that contain links to this reference include
ReturnForecasting6 and
ReturnForecastingRefs
| Reference | Title | Link |
| Ghahramani, Z. (2015) | Probabilistic machine learning and artificial intelligence | here |
Abstract
"How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery." |
See
here to choose a new Category/Sub-Category or
here for a list of all references held by the Nematrian website. Please
contact us if any of the above material is inaccurate or if there are references you think should be included that we have excluded or vice-versa.