CS5487: Machine Learning: Principles and Practice
This course introduces fundamental machine learning principles with an emphasis on mathematical derivation, algorithm design, implementation, and evaluation. Topics include statistical and Bayesian learning, clustering, dimensionality reduction, regression, discriminative classifiers, and kernel methods, with selected connections to modern machine learning.
Course Materials
Syllabus · All downloads are PDFs. Tutorials include solutions.
| Week | Topic | Lecture | Tutorial with Solutions |
|---|---|---|---|
| 1 | Introduction and Mathematical Foundations | ||
| 2 | Parameter Estimation | ||
| 3 | Bayesian Parameter Estimation | ||
| 4 | Parametric Clustering | ||
| 5 | Local Density Estimation and Clustering | ||
| 6 | Modern Machine Learning and Project Directions | Not posted |
Past papers and example code: to be posted.
Assignments
| Assignment | Questions | Solutions |
|---|---|---|
| Assignment 1 | PDF · 9 pages | PDF · 11 pages |
Course Information and Schedule
- Institution
- City University of Hong Kong (Dongguan)
- Term
- Fall 2026 · 3 credits · 13 teaching weeks
- Schedule
- Lecture: Monday, 9:30 AM–11:20 AM
Tutorial: Monday, 11:30 AM–12:20 PM - Venue
- AC5-514
- Instructor
- Kangning Cui · Email · AC4-422
Assessment
- Assignments 30%
- Midterm Examination 10%
- Course Project 30%
- Final Examination 30%
Assignments include theoretical and programming work. The course project applies machine learning to a real-world problem.