1. Introduction  
2. Paradigms and Motivations of Applying Machine Learning in Optical Networks  
2.1. System Architecture and Application Paradigms  
2.2. Motivation and Driving Factors  
3. An Overview of Machine Learning in Optical Networks  
3.1. Supervised Learning  
3.2. Unsupervised Learning  
3.3. Reinforcement Learning  
4. Machine Learning for Intelligent Optical Networks Control and Resource Management  
4.1. Optical Network Traffic and Resource Requirement Prediction  
4.2. Routing in Optical Networks  
4.3. RWA and RSA in Optical Networks  
5. Machine Learning for Intelligent Optical Networks Monitoring and Survivability  
5.1. Optical Performance Monitoring  
5.2. Quality of Transmission Estimation  
5.3. Failure Management  
6. Challenges and Possible Solutions  
6.1. Open Dataset Access  
6.2. Model Interpretability and Traceability  
6.3. Model Generalization Ability  
6.4. Algorithm Computational Complexity  
6.5. System Security and Reliability  
7. Conclusion