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Intermediate Predictive Modeling Limited Attendance Seminar

December 7, 2017 - December 8, 2017

Deloitte LLC
Chicago, IL

Generalized Linear Models (GLMs) and such extensions as Generalized Additive Models and Multilevel/Hierarchical Models are today considered standard elements of the actuarial toolkit. As the size and complexity of available datasets continue to grow, actuaries and data scientists increasingly find it useful to adopt a broader array of statistical and machine learning tools. The Intermediate Predictive Modeling Limited Attendance Seminar [PMLAS-2] is intended for actuaries and data scientists who wish to complement a working knowledge of GLMs with other tools.

The techniques covered in this seminar fall into three classes. First, a variety of methods are available for achieving parameter shrinkage ("credibility weighting") within GLMs. This seminar will cover two highly practical and increasingly popular such methods: ridge and lasso regression. Second, machine learning techniques offer a powerful way of discovering and modeling interactions and nonlinear relationships in complex datasets. The seminar will cover some of the more popular machine learning techniques. Finally, the seminar will cover a variety of unsupervised learning methods (learning methods that do not involve a pre-specified outcome variable). Such methods are useful for identifying clusters and reduced-dimension data features in complex datasets. A common theme of all three classes of techniques is harnessing computing power to make sense of "wide" datasets, which commonly contain hundreds or even thousands of columns.

The seminar is designed to provide a general survey of techniques which complement the GLM framework. A substantive case study, as well as a number of smaller case studies, will complement the theoretical discussion with hands-on experience applying the techniques.

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