Description: In An Introduction to Incremental Learning, by Qiang Wu and I describe how machine learning is a natural enhancement to predictive analytics through its many useful tools to derive meaning from a lot of otherwise unusable data such as handwriting. Again, there are formulas involved; but we have tried to keep them understandable to the actuary who isn’t already familiar with stochastic gradient descent, perceptrons, and principal component analysis. Incremental learning can help you refine the estimations you derive from more traditional actuarial methods.

Description: In this article, we will overview some popular tree-based models and understand how such models may be applied to the insurance industry.

Description: Collaborative filtering models drive recommendations provided by Netflix and Amazon. This article uses the same models to explore patients’ medical conditions.

Description: The editor of the newsletter provides a running list of articles included in the issue highlighting such topics as the applicability genetic algorithms, the power of group dynamics, judgmental forecasting, improvement in mortality and complexity science.

Description: This article discussed the NEAT family of neural network algorithms. NEAT automatically determines the number of hidden neurons, as well as their structure. Additionally HyperNEAT can scale to very large problem domains.

Description: Overview of regularization and least angle regression approaches.

Description: A look into how machine learning models implement classification and regression at a deeper level. Covers topics such as multi-regression, softmax, and ROC charts.

Description: Summary of what the section has accomplished over the past year.

Description: 3rd Annual iPad contest announcement

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.