SDST3612 Statistical machine learning (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota ---
Course Co-ordinator Prof L Yu, SCDS (Department of Statistics and Actuarial Science) < lqyu@hku.hk >
Teachers Involved (Prof L Qu,Statistics & Actuarial Science)
(Prof L Yu,Statistics & Actuarial Science)
Course Objectives Machine learning is the study of computer algorithms that build models of observed data in order to make predictions or decisions. Statistical machine learning emphasizes the importance of statistical methodology in the algorithmic development. This course provides a comprehensive and practical coverage of essential machine learning concepts and a variety of learning algorithms under supervised and unsupervised settings.
Course Contents & Topics Basics of machine learning, linear regression, logistic regression, regularization, cross-validation, tree-based methods, dimension reduction, principal component analysis, cluster analysis, neural network basics and deep models.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 get familiar with the workflow of a data science or machine learning project
CLO 2 understand and apply a wide range of statistical machine learning methods, and recognize their characteristics, strengths and weaknesses
CLO 3 identify and use appropriate techniques for a particular data science project
CLO 4 evaluate the quality of the resulting model in terms of prediction accuracy and model explainability
CLO 5 apply Python programming for solving data-scientific problems
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in SDST3600 or SDST3907, or already enrolled in this course; and
Pass in COMP1117 or ENGG1330 or SDST2604; and
Not for students who have passed in SDST4904, or already enrolled in this course; and
Not for BSc(Actuarial Science) students.
BSc(Actuarial Science) students are advised to take SDST4904 Statistical learning for risk modelling instead.
Recommended: proficiency in Python and programming assignments will require the use of Python
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 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Core/Compulsory )
2024 Major in Decision Analytics ( Core/Compulsory )
2024 Major in Risk Management ( Disciplinary Elective )
2024 Major in Statistics ( Disciplinary Elective )
2024 Minor in Actuarial Studies ( Disciplinary Elective )
2024 Minor in Risk Management ( Disciplinary Elective )
2024 Minor in Statistics ( Disciplinary Elective )
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Core/Compulsory )
2023 Major in Decision Analytics ( Core/Compulsory )
2023 Major in Risk Management ( Disciplinary Elective )
2023 Major in Statistics ( Disciplinary Elective )
2023 Minor in Actuarial Studies ( Disciplinary Elective )
2023 Minor in Risk Management ( Disciplinary Elective )
2023 Minor in Statistics ( Disciplinary Elective )
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Core/Compulsory )
2022 Major in Decision Analytics ( Core/Compulsory )
2022 Major in Risk Management ( Disciplinary Elective )
2022 Major in Statistics ( Disciplinary Elective )
2022 Minor in Actuarial Studies ( Disciplinary Elective )
2022 Minor in Risk Management ( Disciplinary Elective )
2022 Minor in Statistics ( Disciplinary Elective )
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Core/Compulsory )
2021 Major in Decision Analytics ( Core/Compulsory )
2021 Major in Risk Management ( Disciplinary Elective )
2021 Major in Statistics ( Disciplinary Elective )
2021 Minor in Actuarial Studies ( Disciplinary Elective )
2021 Minor in Risk Management ( Disciplinary Elective )
2021 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping 2024 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 1,2,3 >
2024 Major in Decision Analytics < PLO 1,2,3,4,5,6 >
2024 Major in Risk Management < PLO 2,3,4,5 >
2024 Major in Statistics < PLO 1,2,3,4,5,6 >
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 1,2,3 >
2023 Major in Decision Analytics < PLO 1,2,3,4,5,6 >
2023 Major in Risk Management < PLO 2,3,4,5 >
2023 Major in Statistics < PLO 1,2,3,4,5,6 >
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 1,2,3 >
2022 Major in Decision Analytics < PLO 1,2,3,4,5,6 >
2022 Major in Risk Management < PLO 2,3,4,5 >
2022 Major in Statistics < PLO 1,2,3,4,5,6 >
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 1,2,3 >
2021 Major in Decision Analytics < PLO 1,2,3,4,5,6 >
2021 Major in Risk Management < PLO 2,3,4,5 >
2021 Major in Statistics < PLO 1,2,3,4,5,6 >
Offer in 2025 - 2026 Y        1st sem    2nd sem    Examination No Exam     
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 30.0 1,2,3,5
Project reports 40.0 1,2,3,4,5
Test 30.0 2,3
Required/recommended reading
and online materials		 1. James, G., Witten, D., Hastie, T., Tibshirani, R., and Taylor J. (2023). An Introduction to Statistical Learning with Applications in Python, Springer, New York.
https://hastie.su.domains/ISLP/ISLP_website.pdf.download.html
2. Hastie, T., Tibshirani, R., and Friedeman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition, Springer, New York.
https://web.stanford.edu/~hastie/ElemStatLearn/
3. Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn and TensorFlow, OReilly. https://github.com/ageron/handson-ml2
Course Website http://moodle.hku.hk
Additional Course Information