2019 CAS Predictive Modeling Bootcamp

OVERVIEW

The PA Bootcamp will cover topics from both the classical statistics and machine learning paradigms. Ordinary Least Squares [OLS] regression and Generalized Linear Models will be treated as foundation, and covered in depth on Day 1.

• Day 1 will also introduce such foundational concepts as maximum likelihood, penalized likelihood, the bias-variance tradeoff, and out-of-sample statistical validation.
• Day 2 will cover classification modeling (Logistic Regression and Linear/Quadratic Discriminant Analysis), regularized regression (Ridge and Lasso regression), survival analysis, and multilevel/hierarchical models.
• Day 3 will cover such supervised machine learning topics such as Generalized Additive Models, Naïve Bayes, Artificial Neural Networks, Classification and Regression Trees, Random Forests, Support Vector Machines, and Boosting. A conceptual discussion of deep learning neural networks will be provided, time permitting.
• Day 4 (a half-day) will cover such topics in unsupervised learning as k-means Clustering, Hierarchical Clustering, and Principal Components Analysis. Time permitting, other such unsupervised learning topics as Multidimensional Scaling and Self-organizing Maps will also be discussed and illustrated.

The Seminar will be self-contained and designed for beginners with little or no experience with R-based data science. It will feature a blend of lecture, simple illustrative case studies, and more substantial case studies. The simple case studies will be provided in R markdown documents containing code and instructions and suggestions for analyzing simple datasets. These interactive exercises will serve as a “bridge” between theoretical discussions and more complex case studies. Participants will also be encouraged to continue to work on more complex case studies in the evenings.

To this end, the seminar will include a self-contained introduction to the R statistical computing environment. R is one of the most comprehensive and widely used statistical modeling tools. For more on R, see the CAS Microlearning Series on installing and using R. You must complete all the items to certify that you are prepared for the bootcamp.

Content Covered:

• Statistics refresher: maximum Likelihood, simulating data, bootstrapping, simple linear regression
• Generalized Linear Models (Gaussian, Poisson, Gamma, logistic, Tweedie)
• Spline regression and Generalized Additive Models
• Cross-validation and the Bias-Variance Tradeoff
• Methodological concepts such as Exploratory Data Analysis, model design, nested model comparisons, model criticism, missing data, and variable selection will be discussed and illustrated throughout the seminar
• Case study involving models designed for point estimation versus ranking cases. This case study will compare and contrast the construction of rating plans and underwriting models. Practical design considerations depending on the intended use include:
• Modeling data structure and data processing
• Target variable selection and model design
• Structure of predictive variables and predictive variable development
• Model development methodology
• Model validation scenarios and approaches
• Considerations on business implementation

Attendance is limited to a maximum of 35 students. Attendees will be selected on a first-registered, first-accepted basis. Participants are expected to bring their own laptop, loaded with the R software, to the seminar. Instructions on how to install R and perform basic manipulations in R will be provided in advance of the seminar.

LOCATION AND LODGING

Location:

Deloitte LLC
111 S Wacker Dr.
Chicago, IL 60606

Hotel Accommodations:

Hyatt Place Chicago Downtown
28 North Franklin Street
Phone: (312) 955-0950

La Quinta Inn and Suites Downtown
1 South Franklin Street
Phone: (312) 558-1020

JW Marriott Chicago
151 West Adams Street
Phone: (312) 660-8200

Hyatt Centric the Loop Chicago
100 West Monroe Street
Phone: (312) 236-1234

W Chicago City Center
172 West Adams Street
Phone: (312) 332-1200

CONTINUING EDUCATION CREDIT

The CAS Continuing Education Policy applies to all ACAS and FCAS members who provide actuarial services. Actuarial services are defined in the CAS Code of Professional Conduct as “professional services provided to a Principal by an individual acting in the capacity of an actuary. Such services include the rendering of advice, recommendations, findings, or opinions based upon actuarial considerations.” Members who are or could be subject to the continuing education requirements of a national actuarial organization can meet the requirements of the CAS Continuing Education Policy by satisfying the continuing education requirements established by a national actuarial organization recognized by the Policy.

Participants should claim credit commensurate with the extent of their participation in the activity. CAS members earn 1 CE Credit per 50 minutes of educational session time, not to include breaks or lunch.

Note: The amount of CE credit that can be earned for participating in this activity must be assessed by the individual attendee. It also may be different for individuals who are subject to the requirements of organizations other than the American Academy of Actuaries.

CONSENT TO USE OF MULTIMEDIA

Registration and attendance at or participation in CAS meetings, seminars, and other activities constitutes an agreement by the registrant to the CAS use and distribution (both now and in the future) of the registrant or attendee's image or voice in photographs, videotapes, electronic reproduction, and audiotapes of such events and activities.

CONTACT INFORMATION

• For more information on content, please contact Nora Potter, Professional Education Coordinator - International and Online Services at npotter@casact.org.
• For more information on attendee registration, please email the Actuaries' Resource Center at arc@casact.org.
• For more information on the Seminar other than registration or content issues, please email meetings@casact.org.
• For more information on other CAS opportunities or regarding administrative policies such as complaints and refunds, please contact the CAS Office at (703) 276-3100 or office@casact.org.