# Big Data and Artificial Intelligence

New week, I will be in France for a few days. On Monday and Tuesday, I will be in Beaune, in Burgundy, at the first “Rencontres Mutualistes” (I will upload the slides of my talk soon). And on Wednesday, I will be in Paris, at ESCP Europe Business School. I will be giving a two hour lecture on “Big Data and Artificial Intelligence”, to use some buzzwords, as asked. More honestly, it will be on (new) data and (new) algorithms for predictive modeling. Slides are now online.

# Data Science for Actuaries, Regression Models with R

After an introduction to Advanced R, we will discuss for the last part of our crash course visualization and graphs (from the previous set of slides), and I just uploaded additional slides on regression models (including some pdf version)

# Predictive Modeling

Tomorrow, around noon, I will be giving a talk on predictive modeling for actuaries. In the introduction, I will get back shortly on the idea that a prediction is usually a best estimate, in the sense of getting an expected value. And because

$\mathbb{E}(X)=\underset{c\in\mathbb{R}}{\text{argmin}}\{\mathbb{E}\left([X-c]^2\right)\}=\underset{c\in\mathbb{R}}{\text{argmin}}\{\mathbb{E}\left(||X-c||_{L_2}\right)\}$

it is natural to use least square ideas. In order to illustrate all those concepts, we will use a simple dataset, with the sex, the height and the weight of a person, as well as declared weight.

Davis=read.table(
"http://socserv.socsci.mcmaster.ca/jfox/Books/Applied-Regression-2E/datasets/Davis.txt")

Since there is a typo in this dataset, we have to invert to figures

Davis[12,c(2,3)]=Davis[12,c(3,2)]

but it’s not a big deal. The variable of interest, here, is someone’s weight

attach(Davis)
Y=weight*2.204622

(here in pounds). We will use explanatory variables such as the sex of that person, or his/her height

X=Davis\$height / 30.48

(in inches). So, we will start with the (standard) linear model, just to make sure that we all talk about the same thing.

The goal will be to use (possible) explanatory variable to improve our prediction. We will start with the standard linear model, but we will see that nonlinear models can also easily be obtained,

Non linearities will be discussed. But those models are Gaussian (as mentioned above). And homoscedastic. So we will see how generalized linear models can be used to model the mean and the variance, at the same time. For instance, with a Poisson regression (below), the variance will increase with the expected value.

After this general introduction, we will spend some time on 0-1 variables. We will see how to use a logistic regression, and also discuss more generally which kind of models can be used for classification. ROC curves will be presented, and explained.

Then, we will also see an alternative to the logistic model, namely classification trees and CART techniques

We will also discuss random forrests, bagging and boosting techniques

pdf version of the slides can be downloaded.

# SOA Webinar on Predictive Modeling

I will give, with Qichun Xu, a joint webinar for the Reinsurance Council and the Futurism Council of the Society of Actuaries, on Perspectives of Predictive Modeling with Case Studies in a few days. The slides of my talk are now available (I do recommand to open the pdf version of the slides with Acrobat, since there are animated pictures in the slides that could not be visualized below for instance). The Society of Actuaries asked specifically for a powerpoint document, so I will use screenshots of the slides for the webinar. I do encourage to open and read the pdf file for a better quality… Sorry for the inconvenience. I will upload soon lines of codes to reproduce most of the graphs. All comments and remarks are welcome.