SDST4611 High-dimensional Statistical Learning (6 credits) Academic Year 2026
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota
Course Co-ordinator TBC, SCDS (Department of Statistics and Actuarial Science) < ugenq@hku.hk >
Teachers Involved
Course Objectives This course will provide a comprehensive introduction to high-dimensional statistics. Students will learn practical techniques and concepts for handling statistical estimation and inference when faced with large number of variables. These skills are essential for students working with large data sets.
Course Contents & Topics Concepts of Bias-variance tradeoff and overfitting, regularization methods, Lasso, ridge regression, elastic net, variable selection, Bayesian variable selection, AIC, BIC, cross-validation, dimension reduction, principal components analysis, singular value decomposition, Bootstrap.

Real data sets will be presented for gaining hands-on experience in dealing with high-dimensional data.

Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 Understand the concepts of overfitting and bias-variance tradeoff, as well as the idea behind cross-validation and bootstrap
CLO 2 Apply regularization methods to handle high-dimensional data
CLO 3 Understand principal components analysis and singular value decomposition
CLO 4 Apply high-dimensional statistical methods to real-world big data problems
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in SDST3600 or SDST3612
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2026 Professional Core in Decision Analytics ( Core/Compulsory )
2026 Professional Core in Statistics ( Disciplinary Elective )
2026 Major in Decision Analytics ( Core/Compulsory )
2026 Major in Statistics ( Disciplinary Elective )
2026 Minor in Statistics ( Disciplinary Elective )
2025 Professional Core in Decision Analytics ( Core/Compulsory )
2025 Professional Core in Statistics ( Disciplinary Elective )
2025 Major in Decision Analytics ( Core/Compulsory )
2025 Major in Statistics ( Disciplinary Elective )
2025 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping 2026 Professional Core in Decision Analytics < PLO 1,2,3,4,6 >
2026 Professional Core in Statistics < PLO 1,2,4,6 >
2026 Major in Decision Analytics < PLO 1,2,3,4,6 >
2026 Major in Statistics < PLO 1,2,4,6 >
2025 Professional Core in Decision Analytics < PLO 1,2,3,4,6 >
2025 Professional Core in Statistics < PLO 1,2,4,6 >
2025 Major in Decision Analytics < PLO 1,2,3,4,6 >
2025 Major in Statistics < PLO 1,2,4,6 >
Offer in 2026 - 2027 N            Examination      
Offer in 2027 - 2028 N
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, tutorials, and class test(s)) 50.0 1,2,3,4
Examination One 2-hour written examination 50.0 1,2,3,4
Required/recommended reading
and online materials		 Wainwright, M.J. (2019) High-Dimensional Statistics: A Non-Asymptotic Viewpoint. CUP. (https://www.google.com/books/edition/High_Dimensional_Statistics/IluHDwAAQBAJ?hl=en&gbpv=0&kptab=overview)
Course Website http://moodle.hku.hk
Additional Course Information