Interpretable Machine Learning Models

Description
Speaker
Machine Learning models have demonstrated immense success in making accurate predictions from unobserved data. However, many of the successfully deployed models that have high predictive accuracy are usually black box models. Judging performance primarily based on their predictive accuracy potentially lead to technical debt and other fundamental issues, especially in areas of application such as security, nondiscrimination, customer retention, etc.
The objective of this talk is to enlighten the audience about some of the pitfalls in using widely popular ML algorithms and offers some recommendations to address these concerns.
Soma Bhattacharya

Soma Bhattacharya is a data scientist with expertise in causal modeling techniques. She currently leads a team of talented data scientists working on AI focused initiatives for Expedia’s conversation platform for customer engagement. Prior to joining Expedia, she also worked at Amazon, the World Bank and health policy research and is author of numerous publications
  • Date: Mar 12, 10:00 (US Pacific Time)
  • Fee: Free
  • Available Seats: 94 (max 200)
  • Help? Send Question
Watch Recording