Spring 2024 Actuarial Science PS5840 section 001

PREDICTIVE MODELING IN FINANCE & INSURAN

PREDICT MODLNG IN FIN & INSRNC

Call Number 11984
Day & Time
Location
TR 2:40pm-3:55pm
602 Lewisohn Hall
Points 3
Grading Mode Standard
Approvals Required None
Instructor Lina Xu
Type SEMINAR
Method of Instruction In-Person
Course Description

This course introduces to the students, generalized linear models (GLM), time series models, and some popular statistical learning models such as decision trees models as well as random forests and boosting trees.  The aim for GLM is to provide a flexible framework for the analysis and model building using the likelihood techniques for almost any data type. The aim for the statistical learning models is to build and predict or understand data structure (if unsupervised) using statistical learning methods such as tree-based for supervised learning and the Principle Component Analysis and Clustering for unsupervised learning.  It develops a student’s knowledge of the theoretical basis in predictive modeling, computational implementation of the models and their application in finance and insurance. Tools such as cross-validation and techniques such as regularization and dimension reduction for fitting and selecting models are explored.  We also implement these models using a combination of Excel and R.

The class covers the material of Exams, Statistics for Risk Modeling (SRM) and Predictive Analytics (PA) of Society of Actuaries, and some material of Exams, Modern Actuarial Statistics I (MAS-I) and MAS II by the Casualty Actuarial Society. This is a core course for the Actuarial Science students. Students who have already taken and passed the SRM and PA exams administered by the SOA are exempted from this class and can substitute an elective.

Web Site Vergil
Department Actuarial Science
Enrollment 22 students (25 max) as of 12:06PM Thursday, December 12, 2024
Subject Actuarial Science
Number PS5840
Section 001
Division School of Professional Studies
Note PRIORITY TO ACTU; OPEN TO CU. IN-PERSON.
Section key 20241ACTU5840K001