1. Introduction  

2. Foundations of Interpretability  

3. Overview of Visual Interpretability  

4. Visual Discovery of ML Models  

5. General Line Coordinates (GLC)  

6. Visual Methods for Traditional Machine Learning  

7. Traditional Visual Methods for Model Understanding: PCA, t-SNE and related point-to-point methods  

8. Interpreting Deep Learning  

9. Open Problems and Current Research Frontiers  

10. Conclusion  