1. Introduction
2. Data Preparation
2.1. Data Collection
2.2. Data Cleaning
2.3. Data Augmentation
3. Feature Engineering
3.1. Feature Selection
3.2. Feature Construction
3.3. Feature Extraction
4. Model Generation
4.1. Search Space
4.2. Evolutionary Algorithms
4.3. Hyperparameter Optimization
5. Model Evaluation
5.1. Low Fidelity
5.2. Weight Sharing
5.3. Surrogate
5.4. Early Stopping
6. NAS Discussion
6.1. NAS Performance Comparison
6.2. One-stage vs. Two-stage
6.3. One-shot/Weight-sharing
6.4. Joint Hyperparameter and Architecture Optimization
6.5. Resource-aware NAS
7. Open Problems and Future Directions
7.1. Flexible Search Space
7.2. Exploring More Areas
7.3. Interpretability
7.4. Reproducibility
7.5. Robustness
7.6. Joint Hyperparameter and Architecture Optimization
7.7. Complete AutoML Pipeline
7.8. Lifelong Learning
8. Conclusions