DSC5001: Statistical Machine Learning I
This course develops the statistical foundations of machine learning through probability, inference, regression, classification, and unsupervised learning. Topics include parameter estimation and bootstrap, linear and nonlinear regression, smoothing and Gaussian processes, classification, tree-based methods, support vector machines, principal component analysis, and clustering.
Course Information
- Institution
- City University of Hong Kong (Dongguan)
- Term
- Fall 2026 · 3 credits · 13 teaching weeks
- Schedule
- Lecture: Monday, 2:00 PM–3:50 PM
Tutorial: Monday, 4:00 PM–4:50 PM - Venue
- AC5-515
- Instructor
- Kangning Cui · Email · AC4-422
Assessment
- Assignments 10%
- Midterm Examination 20%
- Group Project 20%
- Final Examination 50%
Assignments combine statistical reasoning and implementation. The group project develops and evaluates a statistical machine learning solution for a real-world problem.
Course Materials
- Syllabus
- Lecture notes: to be posted
- Past papers: to be posted
- Example code: to be posted