Machine Learning
2024
5
- Interpretable Machine Learning: Model Explanations, SHAP, and Counterfactual Methods
- Feature Engineering: Feature Selection, Feature Construction, and Dimensionality Reduction
- R mlr3verse: Tasks, Learners, Evaluation, and Tuning
- Advanced Machine Learning: Unsupervised Learning, Sparse Learning, and Semi-Supervised Learning
- Machine Learning Introduction: Supervised Learning, Model Evaluation, and Bayesian Methods