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May 21, 2024
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ACS 4410 - Advanced Predictive Analytics3 lecture hours 0 lab hours 3 credits
Course Description
This course is designed for actuarial science majors to explore advanced predictive analysis. This course will introduce foundational statistical learning techniques and Bayesian statistical analysis. In particular, this course will cover decision trees, random forests, support vector machines, and Bayesian Markov Chain and Monte Carlo method. (prereq: ACS 3420 ) (quarter system prereq: Actuarial Science program director consent)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Construct decision trees for both regression and classification
- Understand statistical learning techniques, both supervised and unsupervised
- Use the advanced predictive analytic techniques to solve business problems
- Apply the Bayesian Markov chain and Monte Carlo methods to real-life problems
- Use statistical software to solve a problem in topics covered in this course
- Interpret and effectively communicate the results of these problems
Prerequisites by Topic
- Multivariable calculus
- Probability theory and application
- Statistics
Course Topics
- Decision trees and Random Forests
- K-nearest neighbors
- Support Vector Machines
- Boosting
- Bayesian analysis
- Markov Chain and Monte Carlo Method
Coordinator
Dr. Won Chul Song
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