AC 3414 - Linear Models and Predictive Analytics

4 lecture hours 0 lab hours 4 credits

Course Description
This course is designed for actuarial science majors to provide them a solid statistical foundation. This course will cover topics that are not included in MA 2410 and MA 2411. It will introduce the theory and practical application of linear models and predictive analytics techniques, which are commonly used for insurance modeling work. (prereq: MA 2410 and MA 2631 and (MA 232 or MA 2323))

Course Learning Outcomes
Upon successful completion of this course, the student will be able to:

Employ fundamental exploratory data analysis on data
Use appropriate linear models such as generalized linear models and generalized additive models for analyzing the data.
Understand the key concepts of dimension reduction using principal components analysis
Apply predictive analytics techniques on real-life problems
Use R or other statistical software to solve a problem in topics covered in this course
Interpret results for various linear models and predictive analytics

Prerequisites by Topic

Calculus (single variable and multivariable)
Probability theory and application
Statistics

Course Topics

Explanatory data analysis
Generalized linear model
Generalized additive models
Principle component analysis
Penalized regression
Decision tree and cluster analysis

Coordinator
Dr. Won Chul Song

Announcement

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Milwaukee School of Engineering

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AC 3414 - Linear Models and Predictive Analytics