1. Introduction  
2. Learning from Model Evaluations  
2.1 Task-Independent Recommendations  
2.2 Configuration Space Design  
2.3 Configuration Transfer  
2.4 Learning Curves  
3. Learning from Task Properties  
3.1 Meta-Features  
3.2 Learning Meta-Features  
3.3 Warm-Starting Optimization from Similar Tasks  
3.4 Meta-Models  
3.5 Pipeline Synthesis  
3.6 To Tune or Not to Tune?  
4. Learning from Prior Models  
4.1 Transfer Learning  
4.2 Meta-Learning in Neural Networks  
4.3 Few-Shot Learning  
4.4 Beyond Supervised Learning  
5. Conclusion