Teaching
My teaching experience, courses, and educational materials
Teaching Philosophy
I believe in fostering an interactive and engaging learning environment where students can develop both theoretical understanding and practical skills.
Active Learning
Encouraging student participation and hands-on experience
Real-world Applications
Connecting theoretical concepts to practical problems
Collaborative Learning
Promoting teamwork and peer-to-peer knowledge sharing
Continuous Improvement
Adapting teaching methods based on student feedback
2026 Academic Year
FIT5212 - Data Analysis for Semi-Structured Data (Monash University)
| Role: | Lecturer (0.33 FTA, 68 hours) |
| Term: | Semester 1 |
| Level: | Postgraduate |
| Faculty: | Faculty of Information Technology |
| Credit Points: | 6 |
Detailed Course Information
Course Description
This unit will explore basic forms of semi-structured data: text, time-sequence data, graphs and multiple relations in a database. Basic machine learning algorithms for these kinds of data will be analysed and applied. Some characteristic industry problems for the application of semi-structured data will also be investigated.
Key Topics Covered
- Appraise what kinds of semi-structured data exist and the problems they present for analysis
- Analyse different kinds of algorithms for different kinds of semi-structured data
- Develop and modify some standard algorithms for semi-structured data
- Examine some characteristic industry problems involving semi-structured data, and analyse the suitability of different algorithms
2025 Academic Year
FIT5201 - Machine learning (Monash University)
| Role: | Guest Lecturer (on EM algorithms and GMMs) |
| Term: | Semester 2 |
| Level: | Postgraduate |
| Faculty: | Faculty of Information Technology |
| Credit Points: | 6 |
Detailed Course Information
Course Description
This unit introduces machine learning and the major kinds of statistical learning models and algorithms used in data analysis. The course covers foundational concepts in machine learning and statistical learning theory, including bias-variance trade-offs, model selection, and how model complexity interplays with performance on unobserved data.
Key Topics Covered
- Linear models for regression and classification (linear basis function models, logistic regression, Bayesian classifiers, generalized linear models)
- Discriminative, probabilistic, and generative models
- Non-parametric models (k-nearest neighbour, Gaussian process regression)
- Clustering algorithms (k-means) and latent variable models (Gaussian mixture model)
- Expectation-maximization algorithms
- Neural networks and deep learning