| Academic Year |
2025 |
| Offering Department |
SCDS (Department of Statistics and Actuarial Science) |
Quota |
|
| Course Co-ordinator |
Prof Long Feng, SCDS (Department of Statistics and Actuarial Science) < lfeng@hku.hk > |
| Teachers Involved |
(Prof Long Feng, Statistics & Actuarial Science) |
| 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 STAT3600 or STAT3612 |
| Offer in 2025 - 2026 |
N
|
Examination |
May
|
| Offer in 2026 - 2027 |
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 |
|