Description: Discusses the three complexity science sessions at the Annual Meeting that were sponsored by the Section.

Description: The author discusses the field of artificial intelligence, as well as some of the key definitions along with some possible applications.

Description: Bayesian networks are one of many models that are useful for predictive modeling. This article introduces the concept of Bayesian network and shows how time-series data can be modeled for a Bayesian network.

Description: Summary of the July, 2013 newsletter with an executive overview of each article.

Description: This article describes the history and in some respects, the future of Big Data, and the tools we can use to handle it for PM and for machine learning. One such tool is Vowpal Wabbit, which sounds like something from a Bugs Bunny and Elmer Fudd cartoon (“Dwat that wabbit”), but it really is a popular approach to process a dataset of any size, as there is no need to load all the data into memory. Big data is forcing us to enhance many of our tools. So is the increased actuarial usage of stochastic-in-stochastic analyses (nested stochastic processes), which can result in unacceptably long program run times. Many of our popular computer languages are not inherently well suited for parallel computations.

Description: Looks at free and inexpensive resources for the novice in predictive analytics, covering the data analysis theory as well as the most commonly used languages/software used in the field.

Description: Ruminations on changing skill sets and professions, with a focus on actuaries and data scientists

Description: Here we break down deep learning into its various conceptual sets so as to have a better intuitive understanding of deep learning.

Description: A description of how to use Deep Learning and TensorFlow

Description: A description of the random generalized linear model (RGLM) and how to use it.