Brief Course Description
1. Course Title:
Statistics for Risk Modeling 2
2. Language of Instruction:
Tiếng Anh
3. Course Code:
MAT508086
4. Credits:
3
5. Course Objectives:
• Provide key concepts of statistical learning and some important algorithms: Principal Component Analysis (PCA), Decision Tree models, and Cluster Analysis.
• Equip students with the knowledge to define terms used to classify the types of modeling problems and methods; compare the common methods of assessing model accuracy and reliability.
• Develop data analysis skills related to risk; students will learn to apply these methods in risk modeling.
• Prepare students for the SRM (Statistics for Risk Modeling) exam by the Society of Actuaries (SOA), USA.
6. Brief Description of Course Content:
The Statistical Modeling of Risk (Part 2) course provides provides a comprehensive introduction to the key concepts of statistical learning and the Python programming language. This course primarily covers Principal Component Analysis (PCA), Decision Tree, and Cluster Analysis. Additionally, the course includes content on methods for testing, comparing, and selecting models. This course is designed in alignment with a syllabus from the SRM (Statistics for Risk Modeling) exam by the Society of Actuaries (SOA), USA.