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MA6630 - Introduction to Statistical Learning

Offering Academic Unit
Department of Mathematics
Credit Units
3
Course Duration
One Semester
Pre-requisite(s)

MA5617
OR a pass in all of the following courses 
PH5102 
BIOS5800 
BIOS5801 

Course Offering Term*:
Semester B 2024/25

* The offering term is subject to change without prior notice
 
Course Aims

Statistical learning is a new interdisciplinary area, which has connections to a variety of subjects including statistics, applied mathematics and computer sciences. It has been successfully applied in pattern recognition, signal processing, data mining, bioinformatics and financial engineering, etc. This course presents an overview of many cutting-edge techniques and algorithms in statistical learning. The covered topics include linear and nonlinear classification and regression, support vector machine, kernel methods, model averaging, boosting, as well as high-dimensional data. This course will provide the students the fundamental ideas and intuition behind modern statistical learning methods.


Assessment (Indicative only, please check the detailed course information)

Continuous Assessment: 40%
Examination: 60%
Examination Duration: 2 hours
 
Detailed Course Information

MA6630.pdf

Useful Links

Department of Mathematics