Student
Machine learning interpretability - Road to mastery - Tutorial
Interpreting complex machine learning models can be difficult. There is a plethora of existing methods, but their meaningfulness and reliability are still hard to evaluate. Moreover, depending on the purpose (debugging, …), a technique in the literature is often more appropriate than others. How do we choose the best approach in the landscape of the existing techniques? This talk is organized as a virtual “walk” through different techniques, from building glass-box, transparent models to black-boxes. In the road to mastery, we will cover both standard approaches and latest research outcomes.