1. MOTIVATION

2. STATISTICAL AND DEEP LEARNING METHODS FOR SOFT SENSOR APPLICATIONS

3. DEEP LEARNING FOR MODELING AND FAULT DETECTION

4. REINFORCEMENT LEARNING

5. HYBRID MODELING METHODOLOGY AND APPLICATIONS

6. ML WITH LIMITED DATA

7. CONCLUSIONS