Listen as Anders Larson, FSA, MAAA talks with Shea Parkes, FSA, MAAA in the first of several episodes about data science programming languages. This episode focuses on R, which is probably the most popular languages for data science and statistical analysis in use today. Learn about the history of R, how to get started using it, strengths and weaknesses, and resources for learning more and enhancing your skills. This is the second in a broader series of episodes focusing on languages, libraries, frameworks and cloud providers.

Direct download: PAF_Intro_to_R.mp3
Category:Predictive Analytics and Futurism -- posted at: 3:50pm CDT

Listen as Anders Larson, FSA, MAAA talks with Shea Parkes, FSA, MAAA about the range of tools available to help implement machine learning. Many of the prior sessions focused on the concepts and theory and only hinted at specific tools and implementations. This episode kicks off a whole new series that will focus on languages, libraries, frameworks and cloud providers. In addition to a high-level overview, this episode also drills a little deeper on Graphical User Interface (GUI) modeling tools such as RapidMiner and KNIME.

Direct download: PAF_Machine_Learning_Tools.mp3
Category:Predictive Analytics and Futurism -- posted at: 11:27am CDT

Listen as Anders Larson, FSA, MAAA talks with Nate Pohle, FSA, MAAA, CERA about his experiences in the world of sports analytics. Anders and Nate discuss the opportunities for actuaries to apply their unique analytical and business-focused skills to sports teams and organizations.

Direct download: PAF_Sports_Analytics.mp3
Category:Predictive Analytics and Futurism -- posted at: 12:23pm CDT

Listen in as Anders Larson, FSA, MAAA, interviews Shea Parkes, FSA, MAAA about Support Vector Machines (SVMs). SVMs are a family of predictive models with a lot of unique quirks. For example, they can focus on getting to a yes/no answer above all else. Also don’t miss the heroic attempt to verbally explain the “kernel trick” that can really kick SVMs’ performance into overdrive.

Direct download: SOA17_37_PAF_09_Support_Vector_Machines.mp3
Category:Predictive Analytics and Futurism -- posted at: 9:21am CDT

Listen in as Anders Larson, FSA, MAAA, interviews Shea Parkes, FSA, MAAA, and Michael Niemerg, FSA, MAAA, about artificial neural networks. Artificial neural networks have gone through a recent renaissance as part of deep learning. Artificial neural networks can now provide amazing accuracy, but they come with an equally amazing amount of complexity. This podcast provides a very high-level overview of everything from their history to modern implementations.

Direct download: SOA17_29_PAF_08__Neural_Nets_Renaissance.mp3
Category:Predictive Analytics and Futurism -- posted at: 11:42am CDT

Listen in as Anders Larson, FSA, MAAA, interviews Shea Parkes, FSA, MAAA about Gradient Boosting Machines (GBMs). Although GBMs share many characteristics with Random Forests, they can often provide better performance at the cost of somewhat higher complexity. This discussion covers topics including underlying theory, tuning hyperparameters and modern implementations.

Direct download: SOA17_19_PAF_07_GBMs.mp3
Category:Predictive Analytics and Futurism -- posted at: 10:42am CDT

Listen in as Anders Larson, FSA, MAAA, interviews Shea Parkes, FSA, MAAA about how to do well at Kaggle. In the last episode they explored what Kaggle is and what makes up the various predictive modeling contests they host. In this episode, Shea shares some tips and general advice on how to perform well in a given Kaggle contest.

Direct download: SOA17_17_PAF_05_How_to_do_Well_at_Kaggle.mp3
Category:Predictive Analytics and Futurism -- posted at: 3:42pm CDT

Listen in as Anders Larson, FSA, MAAA, interviews Shea Parkes, FSA, MAAA about Kaggle. Kaggle is an online community that brings together data scientists from across the globe to participate in predictive modeling contests.

Direct download: SOA17_17_PAF_05_Kaggle_Competition.mp3
Category:Predictive Analytics and Futurism -- posted at: 3:38pm CDT

Listen in as Anders Larson, FSA, MAAA, interviews Shea Parkes, FSA, MAAA about Random Forests.  Random Forests are a very important machine learning algorithm that all practitioners should be very comfortable with. They provide great performance with minimal tuning and headache. As a bonus, this session features what is hopefully much improved audio.

Direct download: SOA_17_02_PAF_02_-_Random_Forests.mp3
Category:Predictive Analytics and Futurism -- posted at: 8:54am CDT

Listen in as Shea Parkes, FSA, MAAA interviews Timothy Paris, FSA, MAAA and Steve Wright, FSA, MAAA from Ruark Consulting.  Tim and Steve have developed predictive analytics expertise at their company over the last few years.  They share stories from that journey and some suggested resources for others wanting to do the same.


Predictive Analytics and Futurism Section: Ensemble Predictive Modeling

Listen in as Anders Larson, FSA, MAAA, interviews Shea Parkes, FSA, MAAA about ensemble predictive modeling.  The most accurate predictive models are not any single predictive model; they are ensembles of different models.  This discussion explores what types of ensembles are common, and some of the theory that explains their surprising power.  Understanding some of this theory can help you identify problems or individual models that might benefit most from ensembling.


Predictive Analytics and Futurism Section: Decision Trees

Listen in as Anders Larson, FSA, MAAA, interviews Shea Parkes, FSA, MAAA about decision trees.  Decision trees are an important class of predictive models that are not often covered in traditional statistics classes.  Although it is becoming more rare to use a single decision tree, they are an important building block in at least two of the most prevalent ensemble learning methods in use today.  This discussion tries to lay the groundwork so the next sessions can focus on RandomForests and Gradient Boosting Machines.


Predictive Analytics and Futurism Section: Penalized Regression

Anders Larson, FSA, MAAA, interviews Shea Parkes, FSA, MAAA, and Michael Niemerg, FSA, MAAA, about penalized regression.  Penalized regression can be thought of as the result of applying actuarial credibility to linear modeling.  They explain the statistical theory behind it and the practical reasons an actuary might apply it.  They also touch on some useful implementations that are freely available as open source libraries for R and Python.


Predictive Analytics and Futurism Section: Predictive Modeling Case Study – Life Insurance Underwriting

Listen in as Shea Parkes, FSA, MAAA, from Milliman interviews Vince Granieri, FSA, MAAA, EA from Predictive Resources.  Vince presents a case study of applying predictive analytics to a classical actuarial problem: life insurance underwriting.  Vince walks us through building up his team, his capabilities and his models to provide more useful solutions with predictive analytics.


Cross Validation and Bootstrapping are two important resampling techniques that aid in training predictive models.  This episode focuses on how to use these techniques to train models that generalize well to new data.


The bias-variance trade-off is a fundamental part of machine learning.  In this episode we explore the concepts behind it, and how it relates closely to actuarial credibility.

Direct download: Bias_Variance_Tradeoff_Final.mp3
Category:Predictive Analytics and Futurism -- posted at: 2:51pm CDT

This podcast introduces Machine Learning to actuaries by comparing and contrasting with concepts they are more familiar with.  It also explores who might benefit most from learning what aspects of Machine Learning.

Direct download: What_is_Machine_Learning_Final.mp3
Category:Predictive Analytics and Futurism -- posted at: 8:47am CDT