Description: Feature article discussing the difficult situation of actuarial forecasting with little or no date.

Description: Alternative sources of learning available for free or at low cost if you wish to round out your F&F education about machine learning, artificial neural networks, genetic algorithms and the millennia of biological advances we draw from when we use these techniques.

Description: Continuation of a tutorial on Fuzzy (vs. Boolean, or 'crisp') logic and how you can use it to create actuarial models that better fit the real world. The article provides basic theory in non-technical terms and shows applications using the R programming language.

Description: Summary of the issue, with emphasis on common sense and accuracy rather than blind precision. Also describes two F&F contests.

Description: A review of the Johns Hopkins Data Science Specialization courses with special emphasis on how they may be useful to actuaries.

Description: This article describes how different types of regression models used for fund modeling can affect the variable annuity's risk exposure report and attribution report.

Description: Summary of the reasons behind our section name change; and a short description of each of the articles in this issue.

Description: A discussion on how to avoid bias when choosing research topics.

Description: Up until recent past, the artificial intelligence portion of data science was looked upon cautiously due to its history of booms and flops . In the latest stream of events, major improvements have taken place in this field and now deep learning, the new leading front for Artificial Intelligence, presents a promising prospect for overcoming problems of big data.

Description: This article goes through some basic data exploration techniques to show how different approaches look.