SDST4904 Statistical learning for risk modelling (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota ---
Course Co-ordinator Dr A Lo, SCDS (Department of Statistics and Actuarial Science) < amb10@hku.hk >
Teachers Involved (Dr A Lo,Statistics & Actuarial Science)
Course Objectives To make sense of the vast and complex data sets that have emerged in insurance and finance, it is essential to have a firm understanding of the basic statistical modelling and prediction techniques. This course introduces some useful predictive analytics techniques, such as principal component analysis, naive Bayes classification, decision tree models, and cluster analysis. The R programming language will be used for actual implementation.
Course Contents & Topics Basics of statistical learning, cross-validation, linear model selection and regularization (subset selection, shrinkage methods, dimensional reduction methods), tree-based methods (decision trees, bagging, boosting, random forests), principal component analysis, naive Bayes classification, cluster analysis (K-means clustering, hierarchical clustering), deep learning, survival analysis, multiple testing.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 understand and apply a wide range of predictive analytics techniques for risk modelling
CLO 2 apply the techniques by using the R programming language and interpret the outputs
CLO 3 recognize and compare the characteristics, strengths and weaknesses of different methods
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in SDST3907 or SDST3600; and
Not for students who have passed in SDST3612, or already enrolled in this course; and
For BSc(Actuarial Science) students only.
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2024 BSc in Actuarial Science ( Core/Compulsory )
2023 BSc in Actuarial Science ( Core/Compulsory )
2022 BSc in Actuarial Science ( Core/Compulsory )
2021 BSc in Actuarial Science ( Core/Compulsory )
Course to PLO Mapping 2024 BSc in Actuarial Science < PLO 1,2,3,4,5 >
2023 BSc in Actuarial Science < PLO 1,2,3,4,5 >
2022 BSc in Actuarial Science < PLO 1,2,3,4,5 >
2021 BSc in Actuarial Science < PLO 1,2,3,4,5 >
Offer in 2025 - 2026 Y        2nd sem    Examination May     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments, class test(s) and computer-based project(s)) 25.0 1,2,3
Examination One 2-hour written examination 75.0 1,2,3
Required/recommended reading
and online materials		 An Introduction to Statistical Learning, with Applications in R, James, Witten, Hastie, Tibshirani, 2021, New York: Springer
Course Website http://moodle.hku.hk
Additional Course Information NIL