COMP5328: Advanced Machine Learning
University of Sydney• 2025 S2
In Progress
MachineLearning
ModelEvaluation
Python
DeepLearning
Explores core machine learning algorithms and theory, focusing on model design, evaluation, improvement, and adaptation, with hands-on implementation using Python libraries.
Learning Outcomes
- Algorithm design & evaluation: Present the design of an ML algorithm, including experimental evaluation and reporting.
- Bias–variance trade-off: Explain and reason about variance, bias, under/overfitting, and regularisation.
- Algorithm analysis & improvement: Analyse representative ML algorithms and identify avenues for performance/robustness improvements.
- Problem framing & model adaptation: Formulate learning problems and adapt existing models to new objectives or constraints.
- Paper-to-code implementation: Implement algorithms described in peer-reviewed research papers.
- Statistical foundations: Understand the statistical principles behind learning algorithm design/adaptation.
- Model suitability: Compare introduced models—their strengths, weaknesses, and appropriate use cases.
Takeaways
Coming soon.