- Overview of popular machine learning algorithms: decision trees, random forests, support vector machines, neural networks, etc.
- Considerations for model selection based on data characteristics and research questions.
- Hyperparameter tuning and cross-validation for optimizing model performance.
- Exploring model interpretability and explainability in the biological context.
- Introduction to ensemble learning and model combination techniques.