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
2025 Academic Year
FIT5201 - Data Analysis Algorithms (Monash University)
| Role: | Guest Lecturer |
| Term: | Semester 1, 2 & Summer (T3) |
| 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