Description: While many know that machine learning is a big deal and even have a general idea of what it is, many do not know what machine learning can do for them. As an alternative to citing achievements or explaining algorithms, this article outlines the challenges that occur in statistics and the solutions that machine learning offers. Some of those challenges include correlated predictors, missing data, data interaction, and prediction intervals. Corresponding solutions include regularization technics, missing value imputation with KNN, tree based models, and cluster analysis for backtesting.

Description: From a session at the Spring regional meeting of the Society of Actuaries held in Atlanta, Georgia, May 24-25, 1999 Presenters offer a preview of an actual Course 7 dealing with applied modeling.

Description: Python is the leading programming language for data science. Python programming is one skill that actuaries should acquire. It facilitates analytic work, and the available visualization tools help to communicate ideas. In this article, I use a traditional actuarial problem, mortality modeling, as an example.

Description: From a session at the Spring regional meeting of the Society of Actuaries held in Atlanta, GA May 24-25, 1999: Year 2000 and Beyond: Society of Actuaries New Course: Applied Modeling Demonstration: Charts 3 & 4 from that presentation

Description: From a session at the Spring regional meeting of the Society of Actuaries held in Atlanta, Georgia, May 24-25, 1999 Year 2000 and Beyond: Society of Actuaries New Course: Applied Modeling Demonstration Charts 1 & 2 from that presentation

Description: Introducing Random Effects into Risk Score Models