Applications are invited for a research associate position in the Department of Mathematics at Imperial College London to work in the area of statistical machine learning with applications in population health. The overall theme of the research is to develop methods in statistical machine learning to study worldwide phenotypes and transitions in multiple health outcomes. The position is funded through an UKRI Medical Research Council grant which involves collaborative research among statisticians and health researchers at Imperial College as well as with a network of scientists from most of the world's countries, which will give the work significant scientific and policy impact and visibility. The post-holder will be based in the vibrant Statistics section of the Department of Mathematics, which is consistently ranked as one of the top in the country for research and has world-class expertise in statistical machine learning, and will collaborate with the Environment and Global Health Research Group (www.globalenvhealth.org) at Imperial School of Public Health. The project will involve the development of Bayesian hierarchical models to identify multimorbidity clusters and investigate the variation in both magnitude and characteristics of these clusters across and within regions of the world.

Just about everyone agrees that data scientists and AI developers are the new superstars of the tech industry. But ask a group of CIOs to define the precise area of expertise for data science-related job titles, and discord becomes the word of the day. As businesses seek actionable insights by hiring teams that include data analysts, data engineers, data scientists, machine learning engineers and deep learning engineers, a key to success is understanding what each role can -- and can't -- do for the business. Read on to learn what your data science and AI experts can be expected to contribute as companies grapple with ever-increasing amounts of data that must be mined to create new paths to innovation. In a perfect world, every company employee and executive works under a well-defined set of duties and responsibilities.

Artificial Intelligence may well be the most potentially transformative technology since the Cloud, but it's clearly become the reigning champion for Tech hype and media buzz. IBM's Watson – a "cognitive" computer capable of answering natural language questions - was developed to compete on Jeopardy, a popular quiz show. In 2011, Watson competed against world champions Brad Rutter and Ken Jennings before a TV audience of millions…and beat them. At the end, Jennings remarked: "I for one welcome our new computer overlords". In fact, the Watson that won Jeopardy was an outcome of decades of research in "Symbolic AI".

Bayes' theorem is one of the most important formulae in the field of mathematical statistics and probability, used to calculate the chances of a particular event occurring based on relevant existing information. Bayesian inference meanwhile leverages Bayes' theorem to update the probability of a hypothesis as additional data becomes available. New York University Assistant Professor Andrew Gordon Wilson addressed this question in his recent paper The Case for Bayesian Deep Learning. Paper Abstract: The key distinguishing property of a Bayesian approach is marginalization instead of optimization, not the prior, or Bayes rule. Bayesian inference is especially compelling for deep neural networks.

They can only give you answers." Big data utilization in economics and the financial world has increased with each passing day. In previous reports, we have discussed issues and opportunities related to big data applications in economics/finance. This piece is a quick summary of a more-detailed report that outlines a framework to utilize machine learning and statistical data mining tools in the economics/financial world with the goal of more accurately predicting recessions. Decision makers have a vital interest in predicting future recessions in order to enact appropriate policy.

They can only give you answers." Big data utilization in economics and the financial world has increased with each passing day. In previous reports, we have discussed issues and opportunities related to big data applications in economics/finance. This piece is a quick summary of a more-detailed report that outlines a framework to utilize machine learning and statistical data mining tools in the economics/financial world with the goal of more accurately predicting recessions. Decision makers have a vital interest in predicting future recessions in order to enact appropriate policy.

Big data utilization in economics and the financial world has increased with every passing day. In previous reports, we have discussed issues and opportunities related to big data applications in economics/finance.1 This report outlines a framework to utilize machine learning and statistical data mining tools in the economics/financial world with the goal of more accurately predicting recessions. Decision makers have a vital interest in predicting future recessions in order to enact appropriate policy. Therefore, to help decision makers, we raise the question: Does machine learning and statistical data mining improve recession prediction accuracy?

MicrosoftML is an R package for machine learning that works in tandem with the RevoScaleR package. A great way to see what MicrosoftML can do is to take a look at the on-line book Machine Learning with the MicrosoftML Package Package by Ali Zaidi. The book is part of Ali's in-person workshop "Statistical Machine Learning with MicrosoftML", and you can find further materials including data and scripts at this Github repository. If you'd like to experience the workshop in person, Ali will be presenting it at the EARL Boston conference on November 1.